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Tan SYC, Tsoukalas T, Javier K, Fazon T, Singh S, Vardy J. Recommendations on the surveillance and supplementation of vitamins and minerals for upper gastrointestinal cancer survivors: a scoping review. J Cancer Surviv 2024:10.1007/s11764-024-01666-4. [PMID: 39207682 DOI: 10.1007/s11764-024-01666-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 08/17/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Early-stage upper gastrointestinal (UGI) cancer patients, after surgery, have altered gastrointestinal functions, compromising their nutritional status and health outcomes. Nutritional care provision to UGI survivors rarely focuses on long-term survivorship. Here, we explore recommendations for surveillance of micronutrient deficiency and supplementation for UGI cancer survivors after surgery. METHODS A scoping review, based on the Joanna Briggs Institute methodology for scoping reviews. Six databases (Medline, Embase, CINAHL, Cochrane, Scopus, and PsycINFO) and 21 cancer-related organisation websites were searched. Publications between 2010 and March 2024 with recommendations aimed at adult UGI cancer (oesophageal, gastric, pancreatic, small bowel, and biliary tract) survivors were included. RESULTS Twenty-six publications met the selection criteria: 11 reviews (8 narrative reviews, 2 systematic, 1 meta-analysis), 7 expert opinions, 6 guidelines, and 2 consensus papers. Twenty-two publications recommended monitoring of micronutrient deficiencies, and 23 suggested supplementation, with 8 lacking details. Most were targeted at patients with gastric cancer (n = 19), followed by pancreatic cancer (n = 7) and oesophageal cancer (n = 3) with none for biliary tract and small bowel cancers. Vitamin B12 and iron were the most consistently recommended micronutrients across the three tumour groups. CONCLUSION Limited publications recommend surveillance of micronutrient status in UGI cancer survivors during the survivorship phase, especially for oesophageal and pancreatic cancer survivors; most were narrative reviews. These recommendations lacked details, and information was inconsistent. IMPLICATIONS FOR CANCER SURVIVORS Long-term UGI cancer survivors are at risk of micronutrient deficiency after surgery. A standardised approach to prevent, monitor, and treat micronutrient deficiencies is needed.
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Affiliation(s)
- Sim Yee Cindy Tan
- Sydney Medical School, University of Sydney, Concord, NSW, Australia.
- Concord Cancer Centre, Concord Hospital, Concord, NSW, Australia.
- Nutrition and Dietetics Department, Concord Hospital, Concord, NSW, Australia.
| | - Tiffany Tsoukalas
- Discipline of Nutrition and Dietetics, Sydney Nursing School, Faculty of Medicine and Health, University of Sydney, Camperdown, Australia
| | - Kirsten Javier
- Cowra Community Health, Cowra Health Service, Cowra, NSW, Australia
| | - Tiffany Fazon
- Psycho-Oncology Cooperative Research Group (PoCOG), School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia
| | - Sheena Singh
- Nutrition and Dietetics Department, Concord Hospital, Concord, NSW, Australia
| | - Janette Vardy
- Sydney Medical School, University of Sydney, Concord, NSW, Australia
- Concord Cancer Centre, Concord Hospital, Concord, NSW, Australia
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Glenton C, Paulsen E, Agarwal S, Gopinathan U, Johansen M, Kyaddondo D, Munabi-Babigumira S, Nabukenya J, Nakityo I, Namaganda R, Namitala J, Neumark T, Nsangi A, Pakenham-Walsh NM, Rashidian A, Royston G, Sewankambo N, Tamrat T, Lewin S. Healthcare workers' informal uses of mobile phones and other mobile devices to support their work: a qualitative evidence synthesis. Cochrane Database Syst Rev 2024; 8:CD015705. [PMID: 39189465 PMCID: PMC11348462 DOI: 10.1002/14651858.cd015705.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
BACKGROUND Healthcare workers sometimes develop their own informal solutions to deliver services. One such solution is to use their personal mobile phones or other mobile devices in ways that are unregulated by their workplace. This can help them carry out their work when their workplace lacks functional formal communication and information systems, but it can also lead to new challenges. OBJECTIVES To explore the views, experiences, and practices of healthcare workers, managers and other professionals working in healthcare services regarding their informal, innovative uses of mobile devices to support their work. SEARCH METHODS We searched MEDLINE, Embase, CINAHL and Scopus on 11 August 2022 for studies published since 2008 in any language. We carried out citation searches and contacted study authors to clarify published information and seek unpublished data. SELECTION CRITERIA We included qualitative studies and mixed-methods studies with a qualitative component. We included studies that explored healthcare workers' views, experiences, and practices regarding mobile phones and other mobile devices, and that included data about healthcare workers' informal use of these devices for work purposes. DATA COLLECTION AND ANALYSIS We extracted data using an extraction form designed for this synthesis, assessed methodological limitations using predefined criteria, and used a thematic synthesis approach to synthesise the data. We used the 'street-level bureaucrat' concept to apply a conceptual lens to our findings and prepare a line of argument that links these findings. We used the GRADE-CERQual approach to assess our confidence in the review findings and the line-of-argument statements. We collaborated with relevant stakeholders when defining the review scope, interpreting the findings, and developing implications for practice. MAIN RESULTS We included 30 studies in the review, published between 2013 and 2022. The studies were from high-, middle- and low-income countries and covered a range of healthcare settings and healthcare worker cadres. Most described mobile phone use as opposed to other mobile devices, such as tablets. We have moderate to high confidence in the statements in the following line of argument. The healthcare workers in this review, like other 'street-level bureaucrats', face a gap between what is expected of them and the resources available to them. To plug this gap, healthcare workers develop their own strategies, including using their own mobile phones, data and airtime. They also use other personal resources, including their personal time when taking and making calls outside working hours, and their personal networks when contacting others for help and advice. In some settings, healthcare workers' personal phone use, although unregulated, has become a normal part of many work processes. Some healthcare workers therefore experience pressure or expectations from colleagues and managers to use their personal phones. Some also feel driven to use their phones at work and at home because of feelings of obligation towards their patients and colleagues. At best, healthcare workers' use of their personal phones, time and networks helps humanise healthcare. It allows healthcare workers to be more flexible, efficient and responsive to the needs of the patient. It can give patients access to individual healthcare workers rather than generic systems and can help patients keep their sensitive information out of the formal system. It also allows healthcare workers to communicate with each other in more personalised, socially appropriate ways than formal systems allow. All of this can strengthen healthcare workers' relationships with community members and colleagues. However, these informal approaches can also replicate existing social hierarchies and deepen existing inequities among healthcare workers. Personal phone use costs healthcare workers money. This is a particular problem for lower-level healthcare workers and healthcare workers in low-income settings as they are likely to be paid less and may have less access to work phones or compensation. Out-of-hours use may also be more of a burden for lower-level healthcare workers, as they may find it harder to ignore calls when they are at home. Healthcare workers with poor access to electricity and the internet are less able to use informal mobile phone solutions, while healthcare workers who lack skills and training in how to appraise unendorsed online information are likely to struggle to identify trustworthy information. Informal digital channels can help healthcare workers expand their networks. But healthcare workers who rely on personal networks to seek help and advice are at a disadvantage if these networks are weak. Healthcare workers' use of their personal resources can also lead to problems for patients and can benefit some patients more than others. For instance, when healthcare workers store and share patient information on their personal phones, the confidentiality of this information may be broken. In addition, healthcare workers may decide to use their personal resources on some types of patients, but not others. Healthcare workers sometimes describe using their personal phones and their personal time and networks to help patients and clients whom they assess as being particularly in need. These decisions are likely to reflect their own values and ideas, for instance about social equity and patient 'worthiness'. But these may not necessarily reflect the goals, ideals and regulations of the formal healthcare system. Finally, informal mobile phone use plugs gaps in the system but can also weaken the system. The storing and sharing of information on personal phones and through informal channels can represent a 'shadow IT' (information technology) system where information about patient flow, logistics, etc., is not recorded in the formal system. Healthcare workers may also be more distracted at work, for instance, by calls from colleagues and family members or by social media use. Such challenges may be particularly difficult for weak healthcare systems. AUTHORS' CONCLUSIONS By finding their own informal solutions to workplace challenges, healthcare workers can be more efficient and more responsive to the needs of patients, colleagues and themselves. But these solutions also have several drawbacks. Efforts to strengthen formal health systems should consider how to retain the benefits of informal solutions and reduce their negative effects.
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Affiliation(s)
- Claire Glenton
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Elizabeth Paulsen
- Department of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Smisha Agarwal
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Center for Global Digital Health Innovation, Johns Hopkins University, Baltimore, USA
| | - Unni Gopinathan
- Global Health Cluster, Norwegian Institute of Public Health, Oslo, Norway
| | - Marit Johansen
- Global Health Cluster, Norwegian Institute of Public Health, Oslo, Norway
| | - David Kyaddondo
- Child Health and Development Centre, Makerere University, Kampala, Uganda
| | - Susan Munabi-Babigumira
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Josephine Nabukenya
- Department of Information Systems, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda
| | - Immaculate Nakityo
- School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Rehema Namaganda
- College of Health Sciences, Makerere University, Kampala, Uganda
| | - Josephine Namitala
- College of Education and External Studies, Department of Adult and Community Education, Makerere University, Kampala, Uganda
- Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Tom Neumark
- Centre for Development and the Environment, University of Oslo, Oslo, Norway
- Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Allen Nsangi
- College of Health Sciences, Makerere University, Kampala, Uganda
| | | | - Arash Rashidian
- Department of Science, Information and Dissemination, WHO Regional Office for the Eastern Mediterranean, Cairo, Egypt
| | | | - Nelson Sewankambo
- School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Tigest Tamrat
- UNDP/UNFPA/UNICEF/World Bank Special Program of Research, Development and Research Training in Human Reproduction (HRP), Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Simon Lewin
- Department of Health Sciences Ålesund, Norwegian University of Science and Technology (NTNU), Ålesund, Norway
- Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa
- Centre for Epidemic Interventions Research (CEIR), Norwegian Institute of Public Health, Oslo, Norway
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Oami T, Okada Y, Sakuraya M, Fukuda T, Shime N, Nakada TA. Efficiency and Workload Reduction of Semi-automated Citation Screening Software for Creating Clinical Practice Guidelines: A Prospective Observational Study. J Epidemiol 2024; 34:380-386. [PMID: 38105001 PMCID: PMC11230876 DOI: 10.2188/jea.je20230227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/26/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND We evaluated the applicability of automated citation screening in developing clinical practice guidelines. METHODS We prospectively compared the efficiency of citation screening between the conventional (Rayyan) and semi-automated (ASReview software) methods. We searched the literature for five clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for the Management of Sepsis and Septic Shock. Objective measurements of the time required to complete citation screening were recorded. Following the first screening round, in the primary analysis, the sensitivity, specificity, positive predictive value, and overall screening time were calculated for both procedures using the semi-automated tool as index and the results of the conventional method as standard reference. In the secondary analysis, the same parameters were compared between the two procedures using the final list of included studies after the second screening session as standard reference. RESULTS Among the five CQs after the first screening session, the highest and lowest sensitivity, specificity, and positive predictive values were 0.241 and 0.795; 0.991 and 1.000; and 0.482 and 0.929, respectively. In the secondary analysis, the highest sensitivity and specificity in the semi-automated citation screening were 1.000 and 0.997, respectively. The overall screening time per 100 studies was significantly shorter with semi-automated than with conventional citation screening. CONCLUSION The potential advantages of the semi-automated method (shorter screening time and higher discriminatory rate for the final list of studies) warrant further validation.
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Affiliation(s)
- Takehiko Oami
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yohei Okada
- Department of Preventive Services, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Health Services and Systems Research, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Masaaki Sakuraya
- Department of Emergency and Intensive Care Medicine, JA Hiroshima General Hospital, Hiroshima, Japan
| | - Tatsuma Fukuda
- Department of Emergency and Critical Care Medicine, Toranomon Hospital, Tokyo, Japan
| | - Nobuaki Shime
- Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Taka-aki Nakada
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
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Affengruber L, Nussbaumer-Streit B, Hamel C, Van der Maten M, Thomas J, Mavergames C, Spijker R, Gartlehner G. Rapid review methods series: Guidance on the use of supportive software. BMJ Evid Based Med 2024; 29:264-271. [PMID: 38242566 PMCID: PMC11287527 DOI: 10.1136/bmjebm-2023-112530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/22/2023] [Indexed: 01/21/2024]
Abstract
This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RRs) use modified systematic review methods to accelerate the review process while maintaining systematic, transparent and reproducible methods. This paper guides how to use supportive software for RRs.We strongly encourage the use of supportive software throughout RR production. Specifically, we recommend (1) using collaborative online platforms that enable working in parallel, allow for real-time project management and centralise review details; (2) using automation software to support, but not entirely replace a human reviewer and human judgement and (3) being transparent in reporting the methodology and potential risk for bias due to the use of supportive software.
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Affiliation(s)
- Lisa Affengruber
- Department for Evidence-based Medicine and Evaluation, Cochrane Austria, University for Continuing Education Krems, Krems, Austria
- Department of Family Medicine, Maastricht University, Maastricht, The Netherlands
| | - Barbara Nussbaumer-Streit
- Department for Evidence-based Medicine and Evaluation, Cochrane Austria, University for Continuing Education Krems, Krems, Austria
| | - Candyce Hamel
- Canadian Association of Radiologists, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Miriam Van der Maten
- Knowledge Institute, Dutch Association of Medical Specialists, Utrecht, The Netherlands
| | - James Thomas
- University College London, UCL Social Research Institute, London, UK
| | | | - Rene Spijker
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gerald Gartlehner
- Department for Evidence-based Medicine and Evaluation, Cochrane Austria, University for Continuing Education Krems, Krems, Austria
- Center for Public Health Methods, RTI International, Research Triangle Park, North Carolina, USA
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5
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Tóth B, Berek L, Gulácsi L, Péntek M, Zrubka Z. Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed. Syst Rev 2024; 13:174. [PMID: 38978132 PMCID: PMC11229257 DOI: 10.1186/s13643-024-02592-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice. METHODS In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies. RESULTS From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs. CONCLUSIONS Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.
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Affiliation(s)
- Barbara Tóth
- Doctoral School of Innovation Management, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - László Berek
- Doctoral School for Safety and Security, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
- University Library, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - László Gulácsi
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - Márta Péntek
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - Zsombor Zrubka
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary.
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Oami T, Okada Y, Nakada TA. Performance of a Large Language Model in Screening Citations. JAMA Netw Open 2024; 7:e2420496. [PMID: 38976267 PMCID: PMC11231796 DOI: 10.1001/jamanetworkopen.2024.20496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/06/2024] [Indexed: 07/09/2024] Open
Abstract
Importance Large language models (LLMs) are promising as tools for citation screening in systematic reviews. However, their applicability has not yet been determined. Objective To evaluate the accuracy and efficiency of an LLM in title and abstract literature screening. Design, Setting, and Participants This prospective diagnostic study used the data from the title and abstract screening process for 5 clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock. The LLM decided to include or exclude citations based on the inclusion and exclusion criteria in terms of patient, population, problem; intervention; comparison; and study design of the selected CQ and was compared with the conventional method for title and abstract screening. This study was conducted from January 7 to 15, 2024. Exposures LLM (GPT-4 Turbo)-assisted citation screening or the conventional method. Main Outcomes and Measures The sensitivity and specificity of the LLM-assisted screening process was calculated, and the full-text screening result using the conventional method was set as the reference standard in the primary analysis. Pooled sensitivity and specificity were also estimated, and screening times of the 2 methods were compared. Results In the conventional citation screening process, 8 of 5634 publications in CQ 1, 4 of 3418 in CQ 2, 4 of 1038 in CQ 3, 17 of 4326 in CQ 4, and 8 of 2253 in CQ 5 were selected. In the primary analysis of 5 CQs, LLM-assisted citation screening demonstrated an integrated sensitivity of 0.75 (95% CI, 0.43 to 0.92) and specificity of 0.99 (95% CI, 0.99 to 0.99). Post hoc modifications to the command prompt improved the integrated sensitivity to 0.91 (95% CI, 0.77 to 0.97) without substantially compromising specificity (0.98 [95% CI, 0.96 to 0.99]). Additionally, LLM-assisted screening was associated with reduced time for processing 100 studies (1.3 minutes vs 17.2 minutes for conventional screening methods; mean difference, -15.25 minutes [95% CI, -17.70 to -12.79 minutes]). Conclusions and Relevance In this prospective diagnostic study investigating the performance of LLM-assisted citation screening, the model demonstrated acceptable sensitivity and reasonably high specificity with reduced processing time. This novel method could potentially enhance efficiency and reduce workload in systematic reviews.
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Affiliation(s)
- Takehiko Oami
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yohei Okada
- Department of Preventive Services, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Health Services and Systems Research, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Taka-Aki Nakada
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
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Khalil H, Pollock D, McInerney P, Evans C, Moraes EB, Godfrey CM, Alexander L, Tricco A, Peters MDJ, Pieper D, Saran A, Ameen D, Taneri PE, Munn Z. Automation tools to support undertaking scoping reviews. Res Synth Methods 2024. [PMID: 38885942 DOI: 10.1002/jrsm.1731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 05/15/2024] [Accepted: 06/02/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVE This paper describes several automation tools and software that can be considered during evidence synthesis projects and provides guidance for their integration in the conduct of scoping reviews. STUDY DESIGN AND SETTING The guidance presented in this work is adapted from the results of a scoping review and consultations with the JBI Scoping Review Methodology group. RESULTS This paper describes several reliable, validated automation tools and software that can be used to enhance the conduct of scoping reviews. Developments in the automation of systematic reviews, and more recently scoping reviews, are continuously evolving. We detail several helpful tools in order of the key steps recommended by the JBI's methodological guidance for undertaking scoping reviews including team establishment, protocol development, searching, de-duplication, screening titles and abstracts, data extraction, data charting, and report writing. While we include several reliable tools and software that can be used for the automation of scoping reviews, there are some limitations to the tools mentioned. For example, some are available in English only and their lack of integration with other tools results in limited interoperability. CONCLUSION This paper highlighted several useful automation tools and software programs to use in undertaking each step of a scoping review. This guidance has the potential to inform collaborative efforts aiming at the development of evidence informed, integrated automation tools and software packages for enhancing the conduct of high-quality scoping reviews.
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Affiliation(s)
- Hanan Khalil
- School of Psychology and Public Health, Department of Public Health, La Trobe University, Melbourne, Australia
- The Queensland Centre of Evidence Based Nursing and Midwifery: A JBI Centre of Excellence, Brisbane, Queensland, Australia
| | - Danielle Pollock
- JBI, University of Adelaide, Adelaide, Australia
- Health Evidence Synthesis, Recommendations and Impact (HESRI), School of Public Health, University of Adelaide, Adelaide, Australia
| | - Patricia McInerney
- The Wits JBI Centre for Evidence-Based Practice: A JBI Centre of Excellence, Faculty of Health Sciences, University of the Witwatersrand, South Africa
| | - Catrin Evans
- The Nottingham Centre for Evidence Based Healthcare: A JBI Centre of Excellence, University of Nottingham, UK
| | - Erica B Moraes
- Nursing School, Department of Nursing Fundamentals and Administration, Federal Fluminense University, Rio de Janeiro, Brazil
- The Brazilian Centre of Evidence-based Healthcare: A JBI Centre of Excellence - JBI, Brazil
| | - Christina M Godfrey
- Queen's Collaboration for Health Care Quality: A JBI Centre of Excellence, Queen's University School of Nursing, Kingston, Ontario, Canada
| | - Lyndsay Alexander
- The Scottish Centre for Evidence-based, Multi-Professional Practice: A JBI Centre of Excellence, Aberdeen, UK
- School of Health Sciences, Robert Gordon University, Aberdeen, UK
| | - Andrea Tricco
- Queen's Collaboration for Health Care Quality: A JBI Centre of Excellence, Queen's University School of Nursing, Kingston, Ontario, Canada
- Epidemiology Division and Institute for Health, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Micah D J Peters
- Health Evidence Synthesis, Recommendations and Impact (HESRI), School of Public Health, University of Adelaide, Adelaide, Australia
- University of South Australia, Clinical and Health Sciences, Rosemary Bryant AO Research Centre, Adelaide, South Australia, Australia
- University of Adelaide, Faculty of Health and Medical Sciences, Adelaide Nursing School, Adelaide, Australia
| | - Dawid Pieper
- Faculty of Health Sciences Brandenburg, Brandenburg Medical School (Theodor Fontane), Institute for Health Services and Health System Research, Rüdersdorf, Germany
- Center for Health Services Research, Brandenburg Medical School (Theodor Fontane), Rüdersdorf, Germany
| | | | - Daniel Ameen
- Faculty of Medicine, Nursing and Health Sciences, School of Medicine, Monash University, Australia
| | - Petek Eylul Taneri
- HRB-Trials Methodology Research Network, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
| | - Zachary Munn
- JBI, University of Adelaide, Adelaide, Australia
- Health Evidence Synthesis, Recommendations and Impact (HESRI), School of Public Health, University of Adelaide, Adelaide, Australia
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8
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Guo Q, Jiang G, Zhao Q, Long Y, Feng K, Gu X, Xu Y, Li Z, Huang J, Du L. Rapid review: A review of methods and recommendations based on current evidence. J Evid Based Med 2024; 17:434-453. [PMID: 38512942 DOI: 10.1111/jebm.12594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 02/28/2024] [Indexed: 03/23/2024]
Abstract
Rapid review (RR) could accelerate the traditional systematic review (SR) process by simplifying or omitting steps using various shortcuts. With the increasing popularity of RR, numerous shortcuts had emerged, but there was no consensus on how to choose the most appropriate ones. This study conducted a literature search in PubMed from inception to December 21, 2023, using terms such as "rapid review" "rapid assessment" "rapid systematic review" and "rapid evaluation". We also scanned the reference lists and performed citation tracking of included impact studies to obtain more included studies. We conducted a narrative synthesis of all RR approaches, shortcuts and studies assessing their effectiveness at each stage of RRs. Based on the current evidence, we provided recommendations on utilizing certain shortcuts in RRs. Ultimately, we identified 185 studies focusing on summarizing RR approaches and shortcuts, or evaluating their impact. There was relatively sufficient evidence to support the use of the following shortcuts in RRs: limiting studies to those published in English-language; conducting abbreviated database searches (e.g., only searching PubMed/MEDLINE, Embase, and CENTRAL); omitting retrieval of grey literature; restricting the search timeframe to the recent 20 years for medical intervention and the recent 15 years for reviewing diagnostic test accuracy; conducting a single screening by an experienced screener. To some extent, the above shortcuts were also applicable to SRs. This study provided a reference for future RR researchers in selecting shortcuts, and it also presented a potential research topic for methodologists.
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Affiliation(s)
- Qiong Guo
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
- West China Medical Publishers, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Guiyu Jiang
- West China School of Public Health, Sichuan University, Chengdu, P. R. China
| | - Qingwen Zhao
- West China School of Public Health, Sichuan University, Chengdu, P. R. China
| | - Youlin Long
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Kun Feng
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Xianlin Gu
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Yihan Xu
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, P. R. China
- Center for education of medical humanities, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Zhengchi Li
- Center for education of medical humanities, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Jin Huang
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Liang Du
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, P. R. China
- West China Medical Publishers, West China Hospital, Sichuan University, Chengdu, P. R. China
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, P. R. China
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9
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Oerbekke MS, Elbers RG, van der Laan MJ, Hooft L. Designing tailored maintenance strategies for systematic reviews and clinical practice guidelines using the Portfolio Maintenance by Test-Treatment (POMBYTT) framework. BMC Med Res Methodol 2024; 24:29. [PMID: 38308228 PMCID: PMC10835980 DOI: 10.1186/s12874-024-02155-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Organizations face diverse contexts and requirements when updating and maintaining their portfolio, or pool, of systematic reviews or clinical practice guidelines they need to manage. We aimed to develop a comprehensive, theoretical framework that might enable the design and tailoring of maintenance strategies for portfolios containing systematic reviews and guidelines. METHODS We employed a conceptual approach combined with a literature review. Components of the diagnostic test-treatment pathway used in clinical healthcare were transferred to develop a framework specifically for systematic review and guideline portfolio maintenance strategies. RESULTS We developed the Portfolio Maintenance by Test-Treatment (POMBYTT) framework comprising diagnosis, staging, management, and monitoring components. To illustrate the framework's components and their elements, we provided examples from both a clinical healthcare test-treatment pathway and a clinical practice guideline maintenance scenario. Additionally, our literature review provided possible examples for the elements in the framework, such as detection variables, detection tests, and detection thresholds. We furthermore provide three example strategies using the framework, of which one was based on living recommendations strategies. CONCLUSIONS The developed framework might support the design of maintenance strategies that could contain multiple options besides updating to manage a portfolio (e.g. withdrawing and archiving), even in the absence of the target condition. By making different choices for variables, tests, test protocols, indications, management options, and monitoring, organizations might tailor their maintenance strategy to suit specific contexts and needs. The framework's elements could potentially aid in the design by being explicit about the operational aspects of maintenance strategies. This might also be helpful for end-users and other stakeholders of systematic reviews and clinical practice guidelines.
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Affiliation(s)
- Michiel S Oerbekke
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- Knowledge Institute of the Dutch Association of Medical Specialists, Utrecht, The Netherlands.
| | - Roy G Elbers
- Department of General Practice, Intellectual Disability Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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10
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Haby MM, Barreto JOM, Kim JYH, Peiris S, Mansilla C, Torres M, Guerrero-Magaña DE, Reveiz L. What are the best methods for rapid reviews of the research evidence? A systematic review of reviews and primary studies. Res Synth Methods 2024; 15:2-20. [PMID: 37696668 DOI: 10.1002/jrsm.1664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/09/2023] [Accepted: 08/07/2023] [Indexed: 09/13/2023]
Abstract
Rapid review methodology aims to facilitate faster conduct of systematic reviews to meet the needs of the decision-maker, while also maintaining quality and credibility. This systematic review aimed to determine the impact of different methodological shortcuts for undertaking rapid reviews on the risk of bias (RoB) of the results of the review. Review stages for which reviews and primary studies were sought included the preparation of a protocol, question formulation, inclusion criteria, searching, selection, data extraction, RoB assessment, synthesis, and reporting. We searched 11 electronic databases in April 2022, and conducted some supplementary searching. Reviewers worked in pairs to screen, select, extract data, and assess the RoB of included reviews and studies. We included 15 systematic reviews, 7 scoping reviews, and 65 primary studies. We found that several commonly used shortcuts in rapid reviews are likely to increase the RoB in the results. These include restrictions based on publication date, use of a single electronic database as a source of studies, and use of a single reviewer for screening titles and abstracts, selecting studies based on the full-text, and for extracting data. Authors of rapid reviews should be transparent in reporting their use of these shortcuts and acknowledge the possibility of them causing bias in the results. This review also highlights shortcuts that can save time without increasing the risk of bias. Further research is needed for both systematic and rapid reviews on faster methods for accurate data extraction and RoB assessment, and on development of more precise search strategies.
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Affiliation(s)
- Michelle M Haby
- Science and Knowledge Unit, Evidence and Intelligence for Action in Health Department, Pan American Health Organization, Washington, DC, USA
- Department of Chemical and Biological Sciences, University of Sonora, Hermosillo, Mexico
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Jenny Yeon Hee Kim
- Science and Knowledge Unit, Evidence and Intelligence for Action in Health Department, Pan American Health Organization, Washington, DC, USA
| | - Sasha Peiris
- Science and Knowledge Unit, Evidence and Intelligence for Action in Health Department, Pan American Health Organization, Washington, DC, USA
| | - Cristián Mansilla
- McMaster Health Forum, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Marcela Torres
- Science and Knowledge Unit, Evidence and Intelligence for Action in Health Department, Pan American Health Organization, Washington, DC, USA
| | - Diego Emmanuel Guerrero-Magaña
- Doctoral Program in Chemical and Biological Sciences and Health, Department of Chemical and Biological Sciences, University of Sonora, Hermosillo, Mexico
| | - Ludovic Reveiz
- Science and Knowledge Unit, Evidence and Intelligence for Action in Health Department, Pan American Health Organization, Washington, DC, USA
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11
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Nussbaumer-Streit B, Sommer I, Hamel C, Devane D, Noel-Storr A, Puljak L, Trivella M, Gartlehner G. Rapid reviews methods series: Guidance on team considerations, study selection, data extraction and risk of bias assessment. BMJ Evid Based Med 2023; 28:418-423. [PMID: 37076266 PMCID: PMC10715469 DOI: 10.1136/bmjebm-2022-112185] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/21/2023] [Indexed: 04/21/2023]
Abstract
This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group (RRMG). Rapid reviews (RRs) use modified systematic review (SR) methods to accelerate the review process while maintaining systematic, transparent and reproducible methods to ensure integrity. This paper addresses considerations around the acceleration of study selection, data extraction and risk of bias (RoB) assessment in RRs. If a RR is being undertaken, review teams should consider using one or more of the following methodological shortcuts: screen a proportion (eg, 20%) of records dually at the title/abstract level until sufficient reviewer agreement is achieved, then proceed with single-reviewer screening; use the same approach for full-text screening; conduct single-data extraction only on the most relevant data points and conduct single-RoB assessment on the most important outcomes, with a second person verifying the data extraction and RoB assessment for completeness and correctness. Where available, extract data and RoB assessments from an existing SR that meets the eligibility criteria.
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Affiliation(s)
- Barbara Nussbaumer-Streit
- Department for Evidence-based Medicine and Evaluation - Cochrane Austria, University of Krems, Krems, Austria
| | - Isolde Sommer
- Department for Evidence-based Medicine and Evaluation - Cochrane Austria, University of Krems, Krems, Austria
| | - Candyce Hamel
- The Canadian Association of Radiologists, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health - Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Declan Devane
- Cochrane Ireland - School of Nursing and Midwifery, University of Galway, Galway, Ireland
- Evidence Synthesis Ireland - School of Nursing and Midwifery, University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network - School of Nursing and Midwifery, University of Galway, Galway, Ireland
| | | | - Livia Puljak
- Centre for Evidence-Based Medicine and Health Care, Catholic University, Zagreb, Croatia
| | - Marialena Trivella
- Department for Evidence-based Medicine and Evaluation - Cochrane Austria, University of Krems, Krems, Austria
- Department of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
| | - Gerald Gartlehner
- Department for Evidence-based Medicine and Evaluation - Cochrane Austria, University of Krems, Krems, Austria
- RTI International, Research Triangle Park, North Carolina, USA
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12
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Dale E, Peacocke EF, Movik E, Voorhoeve A, Ottersen T, Kurowski C, Evans DB, Norheim OF, Gopinathan U. Criteria for the procedural fairness of health financing decisions: a scoping review. Health Policy Plan 2023; 38:i13-i35. [PMID: 37963078 PMCID: PMC10645052 DOI: 10.1093/heapol/czad066] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 06/19/2023] [Accepted: 08/02/2023] [Indexed: 11/16/2023] Open
Abstract
Due to constraints on institutional capacity and financial resources, the road to universal health coverage (UHC) involves difficult policy choices. To assist with these choices, scholars and policy makers have done extensive work on criteria to assess the substantive fairness of health financing policies: their impact on the distribution of rights, duties, benefits and burdens on the path towards UHC. However, less attention has been paid to the procedural fairness of health financing decisions. The Accountability for Reasonableness Framework (A4R), which is widely applied to assess procedural fairness, has primarily been used in priority-setting for purchasing decisions, with revenue mobilization and pooling receiving limited attention. Furthermore, the sufficiency of the A4R framework's four criteria (publicity, relevance, revisions and appeals, and enforcement) has been questioned. Moreover, research in political theory and public administration (including deliberative democracy), public finance, environmental management, psychology, and health financing has examined the key features of procedural fairness, but these insights have not been synthesized into a comprehensive set of criteria for fair decision-making processes in health financing. A systematic study of how these criteria have been applied in decision-making situations related to health financing and in other areas is also lacking. This paper addresses these gaps through a scoping review. It argues that the literature across many disciplines can be synthesized into 10 core criteria with common philosophical foundations. These go beyond A4R and encompass equality, impartiality, consistency over time, reason-giving, transparency, accuracy of information, participation, inclusiveness, revisability and enforcement. These criteria can be used to evaluate and guide decision-making processes for financing UHC across different country income levels and health financing arrangements. The review also presents examples of how these criteria have been applied to decisions in health financing and other sectors.
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Affiliation(s)
- Elina Dale
- Norwegian Institute of Public Health, Sandakerveien 24C, Oslo 0473, Norway
| | | | - Espen Movik
- Norwegian Institute of Public Health, Sandakerveien 24C, Oslo 0473, Norway
| | - Alex Voorhoeve
- Philosophy, Logic and Scientific Method, London School of Economics and Political Science (LSE), Houghton Street, London WC2A 2AE, UK
| | - Trygve Ottersen
- Norwegian Institute of Public Health, Sandakerveien 24C, Oslo 0473, Norway
| | - Christoph Kurowski
- Health, Nutrition and Population, World Bank Group, 1818 H Street, NW, Washington, DC 20433, USA
| | - David B Evans
- Health, Nutrition and Population, World Bank Group, 1818 H Street, NW, Washington, DC 20433, USA
| | - Ole Frithjof Norheim
- Bergen Centre for Ethics and Priority Setting (BCEPS), University of Bergen, Årstadveien 21, Bergen 5018, Norway
| | - Unni Gopinathan
- Norwegian Institute of Public Health, Sandakerveien 24C, Oslo 0473, Norway
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13
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Roth S, Wermer-Colan A. Machine Learning Methods for Systematic Reviews:: A Rapid Scoping Review. Dela J Public Health 2023; 9:40-47. [PMID: 38173960 PMCID: PMC10759980 DOI: 10.32481/djph.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
Objective At the forefront of machine learning research since its inception has been natural language processing, also known as text mining, referring to a wide range of statistical processes for analyzing textual data and retrieving information. In medical fields, text mining has made valuable contributions in unexpected ways, not least by synthesizing data from disparate biomedical studies. This rapid scoping review examines how machine learning methods for text mining can be implemented at the intersection of these disparate fields to improve the workflow and process of conducting systematic reviews in medical research and related academic disciplines. Methods The primary research question that this investigation asked, "what impact does the use of machine learning have on the methods used by systematic review teams to carry out the systematic review process, such as the precision of search strategies, unbiased article selection or data abstraction and/or analysis for systematic reviews and other comprehensive review types of similar methodology?" A literature search was conducted by a medical librarian utilizing multiple databases, a grey literature search and handsearching of the literature. The search was completed on December 4, 2020. Handsearching was done on an ongoing basis with an end date of April 14, 2023. Results The search yielded 23,190 studies after duplicates were removed. As a result, 117 studies (1.70%) met eligibility criteria for inclusion in this rapid scoping review. Conclusions There are several techniques and/or types of machine learning methods in development or that have already been fully developed to assist with the systematic review stages. Combined with human intelligence, these machine learning methods and tools provide promise for making the systematic review process more efficient, saving valuable time for systematic review authors, and increasing the speed in which evidence can be created and placed in the hands of decision makers and the public.
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Affiliation(s)
- Stephanie Roth
- Medical Librarian, Lewis B. Flinn Medical Library, ChristianaCare
| | - Alex Wermer-Colan
- Academic Director, Loretta C. Duckworth Scholars Studio, Temple University Libraries
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14
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Li J, Kabouji J, Bouhadoun S, Tanveer S, Filion KB, Gore G, Josephson CB, Kwon CS, Jette N, Bauer PR, Day GS, Subota A, Roberts JI, Lukmanji S, Sauro K, Ismaili AA, Rahmani F, Chelabi K, Kerdougli Y, Seulami NM, Soumana A, Khalil S, Maynard N, Keezer MR. Sensitivity and specificity of alternative screening methods for systematic reviews using text mining tools. J Clin Epidemiol 2023; 162:72-80. [PMID: 37506951 DOI: 10.1016/j.jclinepi.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/03/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVES To evaluate the impact of text mining (TM) on the sensitivity and specificity of title and abstract screening strategies for systematic reviews (SRs). STUDY DESIGN AND SETTING Twenty reviewers each evaluated a 500-citation set. We compared five screening methods: conventional double screen (CDS), single screen, double screen with TM, combined double screen and single screen with TM, and single screen with TM. Rayyan, Abstrackr, and SWIFT-Review were used for each TM method. The results of a published SR were used as the reference standard. RESULTS The mean sensitivity and specificity achieved by CDS were 97.0% (95% confidence interval [CI]: 94.7, 99.3) and 95.0% (95% CI: 93.0, 97.1). When compared with single screen, CDS provided a greater sensitivity without a decrease in specificity. Rayyan, Abstrackr, and SWIFT-Review identified all relevant studies. Specificity was often higher for TM-assisted methods than that for CDS, although with mean differences of only one-to-two percentage points. For every 500 citations not requiring manual screening, 216 minutes (95% CI: 169, 264) could be saved. CONCLUSION TM-assisted screening methods resulted in similar sensitivity and modestly improved specificity as compared to CDS. The time saved with TM makes this a promising new tool for SR.
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Affiliation(s)
- Jimmy Li
- Neurology Division, Centre Hospitalier de l'Université de Sherbrooke (CHUS), Sherbrooke, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada
| | - Joudy Kabouji
- Department of Pharmacy, University of Laval, Quebec City, Canada
| | - Sarah Bouhadoun
- Department of Neurology, McGill University, Montreal, Canada
| | - Sarah Tanveer
- Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore, MD, USA
| | - Kristian B Filion
- Departments of Medicine and of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada; Centre for Clinical Epidemiology, Jewish General Hospital - Lady Davis Institute, Montreal, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, Canada
| | - Colin Bruce Josephson
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada; Department of Community Health Sciences, University of Calgary, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Center for Health Informatics, University of Calgary, Calgary, Canada
| | - Churl-Su Kwon
- Department of Neurology, Epidemiology, Neurosurgery and the Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
| | - Nathalie Jette
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada; Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | - Prisca Rachel Bauer
- Department of Psychosomatic Medicine and Psychotherapy, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Ann Subota
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; Department of Medicine, University of Calgary, Calgary, Canada
| | - Jodie I Roberts
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | - Sara Lukmanji
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | - Khara Sauro
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; Department of Surgery, University of Calgary, Calgary, Canada; Department of Oncology & Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
| | | | - Feriel Rahmani
- Faculty of Medicine, McGill University, Montreal, Canada
| | | | | | | | - Aminata Soumana
- Department of Family Medicine, McGill University, Montreal, Canada
| | - Sarah Khalil
- Department of Family Medicine, McGill University, Montreal, Canada
| | - Noémie Maynard
- Department of Internal Medicine, McGill University, Montreal, Canada
| | - Mark Robert Keezer
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada; Departments of Medicine and of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada; Department of Neurosciences, Université de Montréal, Montreal, Canada; School of Public Health, Université de Montréal, Montreal, Canada.
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15
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Sharkiya SH. Quality communication can improve patient-centred health outcomes among older patients: a rapid review. BMC Health Serv Res 2023; 23:886. [PMID: 37608376 PMCID: PMC10464255 DOI: 10.1186/s12913-023-09869-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Effective communication is a cornerstone of quality healthcare. Communication helps providers bond with patients, forming therapeutic relationships that benefit patient-centred outcomes. The information exchanged between the provider and patient can help in medical decision-making, such as better self-management. This rapid review investigated the effects of quality and effective communication on patient-centred outcomes among older patients. METHODS Google Scholar, PubMed, Scopus, CINAHL, and PsycINFO were searched using keywords like "effective communication," "elderly," and "well-being." Studies published between 2000 and 2023 describing or investigating communication strategies between older patients (65 years and above) and providers in various healthcare settings were considered for selection. The quality of selected studies was assessed using the GRADE Tool. RESULTS The search strategy yielded seven studies. Five studies were qualitative (two phenomenological study, one ethnography, and two grounded theory studies), one was a cross-sectional observational study, and one was an experimental study. The studies investigated the effects of verbal and nonverbal communication strategies between patients and providers on various patient-centred outcomes, such as patient satisfaction, quality of care, quality of life, and physical and mental health. All the studies reported that various verbal and non-verbal communication strategies positively impacted all patient-centred outcomes. CONCLUSION Although the selected studies supported the positive impact of effective communication with older adults on patient-centred outcomes, they had various methodological setbacks that need to be bridged in the future. Future studies should utilize experimental approaches, generalizable samples, and specific effect size estimates.
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Affiliation(s)
- Samer H Sharkiya
- Faculty of Graduate Studies, Arab American University, 13 Zababdeh, P.O Box 240, Jenin, Palestine.
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16
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Oliveira Dos Santos Á, Sergio da Silva E, Machado Couto L, Valadares Labanca Reis G, Silva Belo V. The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review. J Biomed Inform 2023; 142:104389. [PMID: 37187321 DOI: 10.1016/j.jbi.2023.104389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/11/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceeds the capacity of human-only analysis. In this context, artificial intelligence (AI) and its branches such as machine learning (ML) can be used to facilitate human efforts in analyzing the literature to foster EBM. The present scoping review aimed to examine the use of AI in the automation of biomedical literature survey and analysis with a view to establishing the state-of-the-art and identifying knowledge gaps. MATERIALS AND METHODS Comprehensive searches of the main databases were performed for articles published up to June 2022 and studies were selected according to inclusion and exclusion criteria. Data were extracted from the included articles and the findings categorized. RESULTS The total number of records retrieved from the databases was 12,145, of which 273 were included in the review. Classification of the studies according to the use of AI in evaluating the biomedical literature revealed three main application groups, namely assembly of scientific evidence (n=127; 47%), mining the biomedical literature (n=112; 41%) and quality analysis (n=34; 12%). Most studies addressed the preparation of systematic reviews, while articles focusing on the development of guidelines and evidence synthesis were the least frequent. The biggest knowledge gap was identified within the quality analysis group, particularly regarding methods and tools that assess the strength of recommendation and consistency of evidence. CONCLUSION Our review shows that, despite significant progress in the automation of biomedical literature surveys and analyses in recent years, intense research is needed to fill knowledge gaps on more difficult aspects of ML, deep learning and natural language processing, and to consolidate the use of automation by end-users (biomedical researchers and healthcare professionals).
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Affiliation(s)
| | - Eduardo Sergio da Silva
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
| | - Letícia Machado Couto
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
| | | | - Vinícius Silva Belo
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
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Burgard T, Bittermann A. Reducing Literature Screening Workload With Machine Learning. ZEITSCHRIFT FUR PSYCHOLOGIE-JOURNAL OF PSYCHOLOGY 2023. [DOI: 10.1027/2151-2604/a000509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Abstract. In our era of accelerated accumulation of knowledge, the manual screening of literature for eligibility is increasingly becoming too labor-intensive for summarizing the current state of knowledge in a timely manner. Recent advances in machine learning and natural language processing promise to reduce the screening workload by automatically detecting unseen references with a high probability of inclusion. As a variety of tools have been developed, the current review provides an overview of their characteristics and performance. A systematic search in various databases yielded 488 eligible reports, revealing 15 tools for screening automation that differed in methodology, features, and accessibility. For the review on the performance of screening tools, 21 studies could be included. In comparison to sampling records randomly, active screening with prioritization approximately halves the screening workload. However, a comparison of tools under equal or at least similar conditions is needed to derive clear recommendations.
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Affiliation(s)
- Tanja Burgard
- Research Synthesis Methods, Leibniz Institute for Psychology (ZPID), Trier, Germany
| | - André Bittermann
- Big Data, Leibniz Institute for Psychology (ZPID), Trier, Germany
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18
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Cheung A, Popoff E, Szabo SM. Application of text mining to the development and validation of a geographic search filter to facilitate evidence retrieval in Ovid
MEDLINE
: An example from the United States. Health Info Libr J 2022. [DOI: 10.1111/hir.12471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/11/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Antoinette Cheung
- Broadstreet Health Economics and Outcomes Research Vancouver British Columbia Canada
| | - Evan Popoff
- Broadstreet Health Economics and Outcomes Research Vancouver British Columbia Canada
| | - Shelagh M. Szabo
- Broadstreet Health Economics and Outcomes Research Vancouver British Columbia Canada
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19
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Cierco Jimenez R, Lee T, Rosillo N, Cordova R, Cree IA, Gonzalez A, Indave Ruiz BI. Machine learning computational tools to assist the performance of systematic reviews: A mapping review. BMC Med Res Methodol 2022; 22:322. [PMID: 36522637 PMCID: PMC9756658 DOI: 10.1186/s12874-022-01805-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Due its methodology, SR require significant time and resources to be performed; they also require repetitive steps that may introduce biases and human errors. Machine learning (ML) algorithms therefore present a promising alternative and a potential game changer to speed up and automate the SR process. This review aims to map the current availability of computational tools that use ML techniques to assist in the performance of SR, and to support authors in the selection of the right software for the performance of evidence synthesis. METHODS The mapping review was based on comprehensive searches in electronic databases and software repositories to obtain relevant literature and records, followed by screening for eligibility based on titles, abstracts, and full text by two reviewers. The data extraction consisted of listing and extracting the name and basic characteristics of the included tools, for example a tool's applicability to the various SR stages, pricing options, open-source availability, and type of software. These tools were classified and graphically represented to facilitate the description of our findings. RESULTS A total of 9653 studies and 585 records were obtained from the structured searches performed on selected bibliometric databases and software repositories respectively. After screening, a total of 119 descriptions from publications and records allowed us to identify 63 tools that assist the SR process using ML techniques. CONCLUSIONS This review provides a high-quality map of currently available ML software to assist the performance of SR. ML algorithms are arguably one of the best techniques at present for the automation of SR. The most promising tools were easily accessible and included a high number of user-friendly features permitting the automation of SR and other kinds of evidence synthesis reviews.
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Affiliation(s)
- Ramon Cierco Jimenez
- International Agency for Research on Cancer (IARC/WHO), Evidence Synthesis and Classification Branch, Lyon, France.
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain.
| | - Teresa Lee
- International Agency for Research on Cancer (IARC/WHO), Services to Science and Research Branch, Lyon, France
| | - Nicolás Rosillo
- Servicio de Medicina Preventiva, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Reynalda Cordova
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, Lyon, France
- Department of Nutritional Sciences, University of Vienna, Vienna, Austria
| | - Ian A Cree
- International Agency for Research on Cancer (IARC/WHO), Evidence Synthesis and Classification Branch, Lyon, France
| | - Angel Gonzalez
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Blanca Iciar Indave Ruiz
- International Agency for Research on Cancer (IARC/WHO), Evidence Synthesis and Classification Branch, Lyon, France
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20
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Jin H, Wagner MW, Ertl-Wagner B, Khalvati F. An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology. Can Assoc Radiol J 2022:8465371221144264. [PMID: 36475925 DOI: 10.1177/08465371221144264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Deep learning techniques using convolutional neural networks (CNNs) have been successfully developed for various medical image analysis tasks. However, the skills to understand and develop deep learning models are not usually taught during radiology training, which constitutes a barrier for radiologists looking to integrate machine learning (ML) into their research or clinical practice. In this work, we developed and evaluated an educational graphical user interface (GUI) to construct CNNs for teaching deep learning concepts to radiology trainees. The GUI was developed in Python using the PyQt and PyTorch frameworks. The functionality of the GUI was demonstrated through a binary classification task on a dataset of MR images of the brain. The usability of the GUI was assessed through 45-min user testing sessions with 5 neuroradiologists and neuroradiology fellows, assessing mean task completion times, the System Usability Scale (SUS), and a qualitative questionnaire as metrics. Task completion times were compared against a ML expert who performed the same tasks. After a 20-min introduction to CNNs and a walkthrough of the GUI, users were able to perform all assigned tasks successfully. There was no significant difference in task completion time compared to a ML expert. The educational GUI achieved a score of 82.5 on the SUS, suggesting that the system is highly usable. Users indicated that the GUI seems useful as an educational tool to teach ML topics to radiology trainees. An educational GUI allows interactive teaching in ML that can be incorporated into radiology training.
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Affiliation(s)
- Haiyue Jin
- Division of Engineering Science, University of Toronto, Toronto, ON, Canada
| | - Matthias W Wagner
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Birgit Ertl-Wagner
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Farzad Khalvati
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada,Department of Computer Science, University of Toronto, Toronto, ON, Canada
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21
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KC A, Kong SYJ, Basnet O, Haaland SH, Bhattarai P, Gomo Ø, Gurung R, Ahlsson F, Meinich-Bache Ø, Axelin A, Malla H, Basula YN, Pathak OK, Pokharel SM, Subedi H, Myklebust H. Usability, acceptability and feasibility of a novel technology with visual guidance with video and audio recording during newborn resuscitation: a pilot study. BMJ Health Care Inform 2022; 29:bmjhci-2022-100667. [PMID: 36455992 PMCID: PMC9717377 DOI: 10.1136/bmjhci-2022-100667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 10/21/2022] [Indexed: 02/18/2023] Open
Abstract
OBJECTIVE Inadequate adherence to resuscitation for non-crying infants will have poor outcome and thus rationalise a need for real-time guidance and quality improvement technology. This study assessed the usability, feasibility and acceptability of a novel technology of real-time visual guidance, with sound and video recording during resuscitation. SETTING A public hospital in Nepal. DESIGN A cross-sectional design. INTERVENTION The technology has an infant warmer with light, equipped with a tablet monitor, NeoBeat and upright bag and mask. The tablet records resuscitation activities, ventilation sound, heart rate and display time since birth. Healthcare providers (HCPs) were trained on the technology before piloting. DATA COLLECTION AND ANALYSIS HCPs who had at least 8 weeks of experience using the technology completed a questionnaire on usability, feasibility and acceptability (ranged 1-5 scale). Overall usability score was calculated (ranged 1-100 scale). RESULTS Among the 30 HCPs, 25 consented to the study. The usability score was good with the mean score (SD) of 68.4% (10.4). In terms of feasibility, the participants perceived that they did not receive adequate support from the hospital administration for use of the technology, mean score (SD) of 2.44 (1.56). In terms of acceptability, the information provided in the monitor, that is, time elapsed from birth was easy to understand with mean score (SD) of 4.60 (0.76). CONCLUSION The study demonstrates reasonable usability, feasibility and acceptability of a technological solution that records audio visual events during resuscitation and provides visual guidance to improve care.
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Affiliation(s)
- Ashish KC
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | - So Yeon Joyce Kong
- Department of Women’s and Children’s Health, Laerdal Medical AS, Stavanger, Norway
| | | | | | | | | | - Rejina Gurung
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden,Golden Community, Lalitpur, Nepal
| | - Fredrik Ahlsson
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
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22
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Kozbenko T, Adam N, Lai V, Sandhu S, Kuan J, Flores D, Appleby M, Parker H, Hocking R, Tsaioun K, Yauk C, Wilkins R, Chauhan V. Deploying elements of scoping review methods for Adverse Outcome Pathway development: A space travel case example. Int J Radiat Biol 2022; 98:1777-1788. [PMID: 35939057 DOI: 10.1080/09553002.2022.2110306] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Purpose Health protection agencies require scientific information for evidence-based decision-making and guideline development. However, vetting and collating large quantities of published research to identify relevant high-quality studies is a challenge. One approach to address this issue is the use of Adverse Outcome Pathways (AOPs) that provide a framework to assemble toxicological knowledge into causally linked chains of key events across levels of biological organization to culminate in an adverse health outcome of significance. Traditionally, AOPs have been constructed using a narrative review approach where the collection of evidence that supports each pathway is based on prior knowledge of influential studies that can also be supplemented by individually selecting and reviewing relevant references. Objectives: We aimed to create a protocol for AOP weight of evidence gathering that harnesses elements of both scoping review methods and artificial intelligence tools to increase transparency while reducing bias and workload of human screeners. Methods: To develop this protocol, an existing space-health AOP in the workplan of the Organisation for Economic Co-operation and Development (OECD) AOP program was used as a case example. To balance the benefits of both scoping review tools and narrative approaches, a study protocol outlining a screening and search strategy was developed, and three reference collection workflows were tested to identify the most efficient method to inform weight of evidence. The workflows differed in their literature search strategies, and combinations of software tools used. Results: Across the three tested workflows, over 59 literature searches were completed, retrieving over 34000 references of which over 3300 were human reviewed. The most effective of the three methods used a search strategy with searches across each component of the AOP network, SWIFT Review as a pre-filtering software, and DistillerSR to create structured screening and data extraction forms. This methodology effectively retrieved relevant studies while balancing efficiency in data retrieval without compromising transparency, leading to a well-synthesized evidence base to support the AOP. Conclusions: The workflow is still exploratory in the context of AOP development, and we anticipate adaptations to the protocol with further experience. To further the systematicity, future iterations of the workflow could include structured quality assessment and risk of bias analysis. Overall, the workflow provides a transparent and unbiased approach to support AOP development, which in turn will support the need for rigorous methods to identify relevant scientific evidence while being practical to allow uptake by the broader community.
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Affiliation(s)
- Tatiana Kozbenko
- Health Canada, Ottawa, Ontario, K1A 0K9, Canada.,University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Nadine Adam
- Health Canada, Ottawa, Ontario, K1A 0K9, Canada
| | - Vita Lai
- Health Canada, Ottawa, Ontario, K1A 0K9, Canada
| | | | | | | | | | - Hanna Parker
- University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | | | - Katya Tsaioun
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Carole Yauk
- University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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23
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Hyams TC, Luo L, Hair B, Lee K, Lu Z, Seminara D. Machine Learning Approach to Facilitate Knowledge Synthesis at the Intersection of Liver Cancer, Epidemiology, and Health Disparities Research. JCO Clin Cancer Inform 2022; 6:e2100129. [PMID: 35623021 DOI: 10.1200/cci.21.00129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Liver cancer is a global challenge, and disparities exist across multiple domains and throughout the disease continuum. However, liver cancer's global epidemiology and etiology are shifting, and the literature is rapidly evolving, presenting a challenge to the synthesis of knowledge needed to identify areas of research needs and to develop research agendas focusing on disparities. Machine learning (ML) techniques can be used to semiautomate the literature review process and improve efficiency. In this study, we detail our approach and provide practical benchmarks for the development of a ML approach to classify literature and extract data at the intersection of three fields: liver cancer, health disparities, and epidemiology. METHODS We performed a six-phase process including: training (I), validating (II), confirming (III), and performing error analysis (IV) for a ML classifier. We then developed an extraction model (V) and applied it (VI) to the liver cancer literature identified through PubMed. We present precision, recall, F1, and accuracy metrics for the classifier and extraction models as appropriate for each phase of the process. We also provide the results for the application of our extraction model. RESULTS With limited training data, we achieved a high degree of accuracy for both our classifier and for the extraction model for liver cancer disparities research literature performed using epidemiologic methods. The disparities concept was the most challenging to accurately classify, and concepts that appeared infrequently in our data set were the most difficult to extract. CONCLUSION We provide a roadmap for using ML to classify and extract comprehensive information on multidisciplinary literature. Our technique can be adapted and modified for other cancers or diseases where disparities persist.
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Affiliation(s)
- Travis C Hyams
- Office of the Director, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Ling Luo
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD
| | - Brionna Hair
- Office of the Director, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Kyubum Lee
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD
| | - Daniela Seminara
- Office of the Director, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD
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24
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A text-mining tool generated title-abstract screening workload savings: performance evaluation versus single-human screening. J Clin Epidemiol 2022; 149:53-59. [DOI: 10.1016/j.jclinepi.2022.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 04/13/2022] [Accepted: 05/24/2022] [Indexed: 11/17/2022]
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25
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Hilton Boon M, Burns J, Craig P, Griebler U, Heise TL, Vittal Katikireddi S, Rehfuess E, Shepperd S, Thomson H, Bero L. Value and Challenges of Using Observational Studies in Systematic Reviews of Public Health Interventions. Am J Public Health 2022; 112:548-552. [PMID: 35319925 PMCID: PMC8961824 DOI: 10.2105/ajph.2021.306658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2021] [Indexed: 11/04/2022]
Affiliation(s)
- Michele Hilton Boon
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Jacob Burns
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Peter Craig
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ursula Griebler
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Thomas L Heise
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - S Vittal Katikireddi
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Eva Rehfuess
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Sasha Shepperd
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Hilary Thomson
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Lisa Bero
- Michele Hilton Boon, Peter Craig, S. Vittal Katikireddi, and Hilary Thomson are with the Medical Research Council/Scottish Government Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK. Jacob Burns and Eva Rehfuess are with the Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany. Ursula Griebler is with the Department for Evidence-Based Medicine and Evaluation, Danube University Krems, Krems an der Donau, Austria. Thomas L. Heise is with the Leibniz Institute for Prevention Research and Epidemiology-BIPS, University of Bremen, Bremen, Germany. Sasha Shepperd is with the Nuffield Department of Population Health, University of Oxford, Oxford, UK. Lisa Bero is with the School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
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26
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Schneider J, Hoang L, Kansara Y, Cohen AM, Smalheiser NR. Evaluation of publication type tagging as a strategy to screen randomized controlled trial articles in preparing systematic reviews. JAMIA Open 2022; 5:ooac015. [PMID: 35571360 PMCID: PMC9097760 DOI: 10.1093/jamiaopen/ooac015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 02/06/2021] [Accepted: 03/24/2021] [Indexed: 11/29/2022] Open
Abstract
Objectives To produce a systematic review (SR), reviewers typically screen thousands of titles and abstracts of articles manually to find a small number which are read in full text to find relevant articles included in the final SR. Here, we evaluate a proposed automated probabilistic publication type screening strategy applied to the randomized controlled trial (RCT) articles (i.e., those which present clinical outcome results of RCT studies) included in a corpus of previously published Cochrane reviews. Materials and Methods We selected a random subset of 558 published Cochrane reviews that specified RCT study only inclusion criteria, containing 7113 included articles which could be matched to PubMed identifiers. These were processed by our automated RCT Tagger tool to estimate the probability that each article reports clinical outcomes of a RCT. Results Removing articles with low predictive scores P < 0.01 eliminated 288 included articles, of which only 22 were actually typical RCT articles, and only 18 were actually typical RCT articles that MEDLINE indexed as such. Based on our sample set, this screening strategy led to fewer than 0.05 relevant RCT articles being missed on average per Cochrane SR. Discussion This scenario, based on real SRs, demonstrates that automated tagging can identify RCT articles accurately while maintaining very high recall. However, we also found that even SRs whose inclusion criteria are restricted to RCT studies include not only clinical outcome articles per se, but a variety of ancillary article types as well. Conclusions This encourages further studies learning how best to incorporate automated tagging of additional publication types into SR triage workflows.
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Affiliation(s)
- Jodi Schneider
- School of Information Sciences, University of Illinois
Urbana-Champaign, Champaign, Illinois, USA,Corresponding Author: Jodi Schneider, School of Information
Sciences, University of Illinois Urbana-Champaign, 501 E. Daniel St., MC-493, Champaign,
IL 61820, USA;
| | - Linh Hoang
- School of Information Sciences, University of Illinois
Urbana-Champaign, Champaign, Illinois, USA
| | - Yogeshwar Kansara
- School of Information Sciences, University of Illinois
Urbana-Champaign, Champaign, Illinois, USA
| | - Aaron M Cohen
- Department of Medical Informatics and Clinical Epidemiology (DMICE), School of
Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Neil R Smalheiser
- Department of Psychiatry, College of Medicine, University of Illinois
Chicago, Chicago, Illinois, USA
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27
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Dagenais S, Russo L, Madsen A, Webster J, Becnel L. Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin Pharmacol Ther 2022; 111:77-89. [PMID: 34839524 PMCID: PMC9299990 DOI: 10.1002/cpt.2480] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 10/30/2021] [Indexed: 12/28/2022]
Abstract
Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.
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Affiliation(s)
| | - Leo Russo
- Global Medical Epidemiology, Worldwide Medical and SafetyPfizer IncCollegevillePennsylvaniaUSA
| | - Ann Madsen
- Global Medical Epidemiology, Worldwide Medical and SafetyPfizer IncNew YorkNew YorkUSA
| | - Jen Webster
- Real World EvidencePfizer IncNew YorkNew YorkUSA
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28
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Adam GP, Wallace BC, Trikalinos TA. Semi-automated Tools for Systematic Searches. Methods Mol Biol 2022; 2345:17-40. [PMID: 34550582 DOI: 10.1007/978-1-0716-1566-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Traditionally, literature identification for systematic reviews has relied on a two-step process: first, searching databases to identify potentially relevant citations, and then manually screening those citations. A number of tools have been developed to streamline and semi-automate this process, including tools to generate terms; to visualize and evaluate search queries; to trace citation linkages; to deduplicate, limit, or translate searches across databases; and to prioritize relevant abstracts for screening. Research is ongoing into tools that can unify searching and screening into a single step, and several protype tools have been developed. As this field grows, it is becoming increasingly important to develop and codify methods for evaluating the extent to which these tools fulfill their purpose.
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Affiliation(s)
- Gaelen P Adam
- Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA.
| | - Byron C Wallace
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Thomas A Trikalinos
- Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA
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29
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Hamel C, Hersi M, Kelly SE, Tricco AC, Straus S, Wells G, Pham B, Hutton B. Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses. BMC Med Res Methodol 2021; 21:285. [PMID: 34930132 PMCID: PMC8686081 DOI: 10.1186/s12874-021-01451-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 10/26/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided different situations and considerations for knowledge synthesis teams to consider when using artificial intelligence and AML for title and abstract screening. METHODS We retrospectively evaluated the implementation and performance of AML across a set of ten historically completed systematic reviews. Based upon the findings from this work and in consideration of the barriers we have encountered and navigated during the past 24 months in using these tools prospectively in our research, we discussed and developed a series of practical recommendations for research teams to consider in seeking to implement AML tools for citation screening into their workflow. RESULTS We developed a seven-step framework and provide guidance for when and how to integrate artificial intelligence and AML into the title and abstract screening process. Steps include: (1) Consulting with Knowledge user/Expert Panel; (2) Developing the search strategy; (3) Preparing your review team; (4) Preparing your database; (5) Building the initial training set; (6) Ongoing screening; and (7) Truncating screening. During Step 6 and/or 7, you may also choose to optimize your team, by shifting some members to other review stages (e.g., full-text screening, data extraction). CONCLUSION Artificial intelligence and, more specifically, AML are well-developed tools for title and abstract screening and can be integrated into the screening process in several ways. Regardless of the method chosen, transparent reporting of these methods is critical for future studies evaluating artificial intelligence and AML.
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Affiliation(s)
- Candyce Hamel
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario Canada
| | - Mona Hersi
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario Canada
| | - Shannon E. Kelly
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario Canada
| | - Andrea C. Tricco
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON Canada
- Epidemiology Division and Institute for Health, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario Canada
| | - Sharon Straus
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON Canada
- Department of Medicine, University of Toronto, Toronto, ON Canada
| | - George Wells
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario Canada
| | - Ba’ Pham
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON Canada
| | - Brian Hutton
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario Canada
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Khalil H, Ameen D, Zarnegar A. Tools to support the automation of systematic reviews: A scoping review. J Clin Epidemiol 2021; 144:22-42. [PMID: 34896236 DOI: 10.1016/j.jclinepi.2021.12.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/09/2021] [Accepted: 12/02/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVE The objectives of this scoping review are to identify the reliability and validity of the available tools, their limitations and any recommendations to further improve the use of these tools. STUDY DESIGN A scoping review methodology was followed to map the literature published on the challenges and solutions of conducting evidence synthesis using the JBI scoping review methodology. RESULTS A total of 47 publications were included in the review. The current scoping review identified that LitSuggest, Rayyan, Abstractr, BIBOT, R software, RobotAnalyst, DistillerSR, ExaCT and NetMetaXL have potential to be used for the automation of systematic reviews. However, they are not without limitations. The review also identified other studies that employed algorithms that have not yet been developed into user friendly tools. Some of these algorithms showed high validity and reliability but their use is conditional on user knowledge of computer science and algorithms. CONCLUSION Abstract screening has reached maturity; data extraction is still an active area. Developing methods to semi-automate different steps of evidence synthesis via machine learning remains an important research direction. Also, it is important to move from the research prototypes currently available to professionally maintained platforms.
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Affiliation(s)
- Hanan Khalil
- School of Psychology and Public Health, Department of Public Health, La Trobe University, Melbourne Campus, Victoria, Australia.
| | - Daniel Ameen
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Wellington Road, Clayton Vic 3168, Australia
| | - Armita Zarnegar
- School of Psychology and Public Health, Department of Public Health, La Trobe University, Melbourne Campus, Victoria, Australia.
- School of Science, Computing and engineering technologies, Swinburne University of Technology, Melbourne, Australia
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Bozada T, Borden J, Workman J, Del Cid M, Malinowski J, Luechtefeld T. Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review. Front Artif Intell 2021; 4:685298. [PMID: 34423285 PMCID: PMC8374944 DOI: 10.3389/frai.2021.685298] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 07/13/2021] [Indexed: 11/16/2022] Open
Abstract
Well-curated datasets are essential to evidence based decision making and to the integration of artificial intelligence with human reasoning across disciplines. However, many sources of data remain siloed, unstructured, and/or unavailable for complementary and secondary research. Sysrev was developed to address these issues. First, Sysrev was built to aid in systematic evidence reviews (SER), where digital documents are evaluated according to a well defined process, and where Sysrev provides an easy to access, publicly available and free platform for collaborating in SER projects. Secondly, Sysrev addresses the issue of unstructured, siloed, and inaccessible data in the context of generalized data extraction, where human and machine learning algorithms are combined to extract insights and evidence for better decision making across disciplines. Sysrev uses FAIR - Findability, Accessibility, Interoperability, and Reuse of digital assets - as primary principles in design. Sysrev was developed primarily because of an observed need to reduce redundancy, reduce inefficient use of human time and increase the impact of evidence based decision making. This publication is an introduction to Sysrev as a novel technology, with an overview of the features, motivations and use cases of the tool. Methods: Sysrev. com is a FAIR motivated web platform for data curation and SER. Sysrev allows users to create data curation projects called "sysrevs" wherein users upload documents, define review tasks, recruit reviewers, perform review tasks, and automate review tasks. Conclusion: Sysrev is a web application designed to facilitate data curation and SERs. Thousands of publicly accessible Sysrev projects have been created, accommodating research in a wide variety of disciplines. Described use cases include data curation, managed reviews, and SERs.
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Affiliation(s)
| | | | | | | | | | - Thomas Luechtefeld
- Insilica LLC, Bethesda, MD, United States
- Toxtrack LLC, Baltimore, MD, United States
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Clark J, McFarlane C, Cleo G, Ishikawa Ramos C, Marshall S. The Impact of Systematic Review Automation Tools on Methodological Quality and Time Taken to Complete Systematic Review Tasks: Case Study. JMIR MEDICAL EDUCATION 2021; 7:e24418. [PMID: 34057072 PMCID: PMC8204237 DOI: 10.2196/24418] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 03/03/2021] [Accepted: 04/04/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Systematic reviews (SRs) are considered the highest level of evidence to answer research questions; however, they are time and resource intensive. OBJECTIVE When comparing SR tasks done manually, using standard methods, versus those same SR tasks done using automated tools, (1) what is the difference in time to complete the SR task and (2) what is the impact on the error rate of the SR task? METHODS A case study compared specific tasks done during the conduct of an SR on prebiotic, probiotic, and synbiotic supplementation in chronic kidney disease. Two participants (manual team) conducted the SR using current methods, comprising a total of 16 tasks. Another two participants (automation team) conducted the tasks where a systematic review automation (SRA) tool was available, comprising of a total of six tasks. The time taken and error rate of the six tasks that were completed by both teams were compared. RESULTS The approximate time for the manual team to produce a draft of the background, methods, and results sections of the SR was 126 hours. For the six tasks in which times were compared, the manual team spent 2493 minutes (42 hours) on the tasks, compared to 708 minutes (12 hours) spent by the automation team. The manual team had a higher error rate in two of the six tasks-regarding Task 5: Run the systematic search, the manual team made eight errors versus three errors made by the automation team; regarding Task 12: Assess the risk of bias, 25 assessments differed from a reference standard for the manual team compared to 20 differences for the automation team. The manual team had a lower error rate in one of the six tasks-regarding Task 6: Deduplicate search results, the manual team removed one unique study and missed zero duplicates versus the automation team who removed two unique studies and missed seven duplicates. Error rates were similar for the two remaining compared tasks-regarding Task 7: Screen the titles and abstracts and Task 9: Screen the full text, zero relevant studies were excluded by both teams. One task could not be compared between groups-Task 8: Find the full text. CONCLUSIONS For the majority of SR tasks where an SRA tool was used, the time required to complete that task was reduced for novice researchers while methodological quality was maintained.
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Affiliation(s)
- Justin Clark
- Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Australia
| | - Catherine McFarlane
- Bond University Nutrition & Dietetics Research Group, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia
- Renal Department, Sunshine Coast University Hospital, Birtinya, Australia
| | - Gina Cleo
- Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Australia
| | - Christiane Ishikawa Ramos
- Bond University Nutrition & Dietetics Research Group, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia
- Nutrition Programme, Federal University of Sao Paulo, Sao Paulo, Brazil
| | - Skye Marshall
- Bond University Nutrition & Dietetics Research Group, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia
- Department of Science, Nutrition Research Australia, Sydney, Australia
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van Haastrecht M, Sarhan I, Yigit Ozkan B, Brinkhuis M, Spruit M. SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing. Front Res Metr Anal 2021; 6:685591. [PMID: 34124534 PMCID: PMC8193570 DOI: 10.3389/frma.2021.685591] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/12/2021] [Indexed: 11/28/2022] Open
Abstract
Research output has grown significantly in recent years, often making it difficult to see the forest for the trees. Systematic reviews are the natural scientific tool to provide clarity in these situations. However, they are protracted processes that require expertise to execute. These are problematic characteristics in a constantly changing environment. To solve these challenges, we introduce an innovative systematic review methodology: SYMBALS. SYMBALS blends the traditional method of backward snowballing with the machine learning method of active learning. We applied our methodology in a case study, demonstrating its ability to swiftly yield broad research coverage. We proved the validity of our method using a replication study, where SYMBALS was shown to accelerate title and abstract screening by a factor of 6. Additionally, four benchmarking experiments demonstrated the ability of our methodology to outperform the state-of-the-art systematic review methodology FAST2.
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Affiliation(s)
- Max van Haastrecht
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Injy Sarhan
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.,Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria, Egypt
| | - Bilge Yigit Ozkan
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Matthieu Brinkhuis
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Marco Spruit
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.,Department of Public Health and Primary Care, Leiden University Medical Center (LUMC), Leiden, Netherlands.,Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, Netherlands
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Chai KEK, Lines RLJ, Gucciardi DF, Ng L. Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews. Syst Rev 2021; 10:93. [PMID: 33795003 PMCID: PMC8017894 DOI: 10.1186/s13643-021-01635-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 03/11/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening. METHODS Three sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples. RESULTS Research Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers. CONCLUSIONS In light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.
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Affiliation(s)
- Kevin E K Chai
- Curtin Institute for Computation, Curtin University, Perth, Australia
- School of Population Health, Curtin University, Perth, Australia
| | - Robin L J Lines
- School of Allied Health, Curtin University, Perth, Australia
| | | | - Leo Ng
- School of Allied Health, Curtin University, Perth, Australia.
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Gates A, Gates M, DaRosa D, Elliott SA, Pillay J, Rahman S, Vandermeer B, Hartling L. Decoding semi-automated title-abstract screening: findings from a convenience sample of reviews. Syst Rev 2020; 9:272. [PMID: 33243276 PMCID: PMC7694314 DOI: 10.1186/s13643-020-01528-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 11/11/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening and explored whether Abstrackr's predictions varied by review or study-level characteristics. METHODS For a convenience sample of 16 reviews for which adequate data were available to address our objectives (11 systematic reviews and 5 rapid reviews), we screened a 200-record training set in Abstrackr and downloaded the relevance (relevant or irrelevant) of the remaining records, as predicted by the tool. We retrospectively simulated the liberal-accelerated screening approach. We estimated the time savings and proportion missed compared with dual independent screening. For reviews with pairwise meta-analyses, we evaluated changes to the pooled effects after removing the missed studies. We explored whether the tool's predictions varied by review and study-level characteristics. RESULTS Using the ML-assisted liberal-accelerated approach, we wrongly excluded 0 to 3 (0 to 14%) records that were included in the final reports, but saved a median (IQR) 26 (9, 42) h of screening time. One missed study was included in eight pairwise meta-analyses in one systematic review. The pooled effect for just one of those meta-analyses changed considerably (from MD (95% CI) - 1.53 (- 2.92, - 0.15) to - 1.17 (- 2.70, 0.36)). Of 802 records in the final reports, 87% were correctly predicted as relevant. The correctness of the predictions did not differ by review (systematic or rapid, P = 0.37) or intervention type (simple or complex, P = 0.47). The predictions were more often correct in reviews with multiple (89%) vs. single (83%) research questions (P = 0.01), or that included only trials (95%) vs. multiple designs (86%) (P = 0.003). At the study level, trials (91%), mixed methods (100%), and qualitative (93%) studies were more often correctly predicted as relevant compared with observational studies (79%) or reviews (83%) (P = 0.0006). Studies at high or unclear (88%) vs. low risk of bias (80%) (P = 0.039), and those published more recently (mean (SD) 2008 (7) vs. 2006 (10), P = 0.02) were more often correctly predicted as relevant. CONCLUSION Our screening approach saved time and may be suitable in conditions where the limited risk of missing relevant records is acceptable. Several of our findings are paradoxical and require further study to fully understand the tasks to which ML-assisted screening is best suited. The findings should be interpreted in light of the fact that the protocol was prepared for the funder, but not published a priori. Because we used a convenience sample, the findings may be prone to selection bias. The results may not be generalizable to other samples of reviews, ML tools, or screening approaches. The small number of missed studies across reviews with pairwise meta-analyses hindered strong conclusions about the effect of missed studies on the results and conclusions of systematic reviews.
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Affiliation(s)
- Allison Gates
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Michelle Gates
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Daniel DaRosa
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Sarah A. Elliott
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Jennifer Pillay
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Sholeh Rahman
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Ben Vandermeer
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
| | - Lisa Hartling
- Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, Department of Pediatrics, University of Alberta, Edmonton, Alberta Canada
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An evaluation of DistillerSR's machine learning-based prioritization tool for title/abstract screening - impact on reviewer-relevant outcomes. BMC Med Res Methodol 2020; 20:256. [PMID: 33059590 PMCID: PMC7559198 DOI: 10.1186/s12874-020-01129-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/22/2020] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Systematic reviews often require substantial resources, partially due to the large number of records identified during searching. Although artificial intelligence may not be ready to fully replace human reviewers, it may accelerate and reduce the screening burden. Using DistillerSR (May 2020 release), we evaluated the performance of the prioritization simulation tool to determine the reduction in screening burden and time savings. METHODS Using a true recall @ 95%, response sets from 10 completed systematic reviews were used to evaluate: (i) the reduction of screening burden; (ii) the accuracy of the prioritization algorithm; and (iii) the hours saved when a modified screening approach was implemented. To account for variation in the simulations, and to introduce randomness (through shuffling the references), 10 simulations were run for each review. Means, standard deviations, medians and interquartile ranges (IQR) are presented. RESULTS Among the 10 systematic reviews, using true recall @ 95% there was a median reduction in screening burden of 47.1% (IQR: 37.5 to 58.0%). A median of 41.2% (IQR: 33.4 to 46.9%) of the excluded records needed to be screened to achieve true recall @ 95%. The median title/abstract screening hours saved using a modified screening approach at a true recall @ 95% was 29.8 h (IQR: 28.1 to 74.7 h). This was increased to a median of 36 h (IQR: 32.2 to 79.7 h) when considering the time saved not retrieving and screening full texts of the remaining 5% of records not yet identified as included at title/abstract. Among the 100 simulations (10 simulations per review), none of these 5% of records were a final included study in the systematic review. The reduction in screening burden to achieve true recall @ 95% compared to @ 100% resulted in a reduced screening burden median of 40.6% (IQR: 38.3 to 54.2%). CONCLUSIONS The prioritization tool in DistillerSR can reduce screening burden. A modified or stop screening approach once a true recall @ 95% is achieved appears to be a valid method for rapid reviews, and perhaps systematic reviews. This needs to be further evaluated in prospective reviews using the estimated recall.
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Yan K, Balijepalli C, Druyts E. Is it always possible to complete a systematic review in 2 weeks? Further thoughts and considerations. J Clin Epidemiol 2020; 126:162-163. [DOI: 10.1016/j.jclinepi.2020.06.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 06/29/2020] [Indexed: 10/24/2022]
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Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. Challenges and opportunities for public health made possible by advances in natural language processing. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2020; 46:161-168. [PMID: 32673380 PMCID: PMC7343054 DOI: 10.14745/ccdr.v46i06a02] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Natural language processing (NLP) is a subfield of artificial intelligence devoted to understanding and generation of language. The recent advances in NLP technologies are enabling rapid analysis of vast amounts of text, thereby creating opportunities for health research and evidence-informed decision making. The analysis and data extraction from scientific literature, technical reports, health records, social media, surveys, registries and other documents can support core public health functions including the enhancement of existing surveillance systems (e.g. through faster identification of diseases and risk factors/at-risk populations), disease prevention strategies (e.g. through more efficient evaluation of the safety and effectiveness of interventions) and health promotion efforts (e.g. by providing the ability to obtain expert-level answers to any health related question). NLP is emerging as an important tool that can assist public health authorities in decreasing the burden of health inequality/inequity in the population. The purpose of this paper is to provide some notable examples of both the potential applications and challenges of NLP use in public health.
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Affiliation(s)
- Oliver Baclic
- Centre for Immunization and Respiratory Infectious Disease, Public Health Agency of Canada, Ottawa, ON
| | - Matthew Tunis
- Centre for Immunization and Respiratory Infectious Disease, Public Health Agency of Canada, Ottawa, ON
| | - Kelsey Young
- Centre for Immunization and Respiratory Infectious Disease, Public Health Agency of Canada, Ottawa, ON
| | - Coraline Doan
- Data, Partnerships and Innovation Hub, Public Health Agency of Canada, Ottawa, ON
| | - Howard Swerdfeger
- Data, Partnerships and Innovation Hub, Public Health Agency of Canada, Ottawa, ON
| | - Justin Schonfeld
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB
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Gates A, Gates M, Sebastianski M, Guitard S, Elliott SA, Hartling L. The semi-automation of title and abstract screening: a retrospective exploration of ways to leverage Abstrackr's relevance predictions in systematic and rapid reviews. BMC Med Res Methodol 2020; 20:139. [PMID: 32493228 PMCID: PMC7268596 DOI: 10.1186/s12874-020-01031-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 05/24/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND We investigated the feasibility of using a machine learning tool's relevance predictions to expedite title and abstract screening. METHODS We subjected 11 systematic reviews and six rapid reviews to four retrospective screening simulations (automated and semi-automated approaches to single-reviewer and dual independent screening) in Abstrackr, a freely-available machine learning software. We calculated the proportion missed, workload savings, and time savings compared to single-reviewer and dual independent screening by human reviewers. We performed cited reference searches to determine if missed studies would be identified via reference list scanning. RESULTS For systematic reviews, the semi-automated, dual independent screening approach provided the best balance of time savings (median (range) 20 (3-82) hours) and reliability (median (range) proportion missed records, 1 (0-14)%). The cited references search identified 59% (n = 10/17) of the records missed. For the rapid reviews, the fully and semi-automated approaches saved time (median (range) 9 (2-18) hours and 3 (1-10) hours, respectively), but less so than for the systematic reviews. The median (range) proportion missed records for both approaches was 6 (0-22)%. CONCLUSION Using Abstrackr to assist one of two reviewers in systematic reviews saves time with little risk of missing relevant records. Many missed records would be identified via other means.
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Affiliation(s)
- Allison Gates
- Alberta Research Centre for Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada.
| | - Michelle Gates
- Alberta Research Centre for Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Meghan Sebastianski
- Alberta Strategy for Patient-Oriented Research (SPOR) SUPPORT Unit Knowledge Translation Platform, University of Alberta, Edmonton, Alberta, Canada
| | - Samantha Guitard
- Alberta Research Centre for Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Sarah A Elliott
- Alberta Research Centre for Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Lisa Hartling
- Alberta Research Centre for Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
- Alberta Strategy for Patient-Oriented Research (SPOR) SUPPORT Unit Knowledge Translation Platform, University of Alberta, Edmonton, Alberta, Canada
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