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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:bmjebm-2023-112530. [PMID: 38242566 DOI: 10.1136/bmjebm-2023-112530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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Shemilt I, Noel-Storr A, Thomas J, Featherstone R, Mavergames C. Machine learning reduced workload for the Cochrane COVID-19 Study Register: development and evaluation of the Cochrane COVID-19 Study Classifier. Syst Rev 2022; 11:15. [PMID: 35065679 PMCID: PMC8783177 DOI: 10.1186/s13643-021-01880-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/26/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies. METHODS A ML classifier for retrieving COVID-19 research studies (the 'Cochrane COVID-19 Study Classifier') was developed using a data set of title-abstract records 'included' in, or 'excluded' from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records 'included' in, or 'excluded' from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records 'included' in, or 'excluded' from, the CCSR between the 4th and 19th of January 2021. RESULTS The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were 'included' in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were 'included' in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded). CONCLUSIONS The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register.
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Affiliation(s)
- Ian Shemilt
- EPPI Centre, UCL Social Research Institute, University College London, 18 Woburn Square, London, WC1H 0NR, UK
| | - Anna Noel-Storr
- Radcliffe Department of Medicine, University of Oxford, Level 4, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK.
| | - James Thomas
- EPPI Centre, UCL Social Research Institute, University College London, 18 Woburn Square, London, WC1H 0NR, UK
| | - Robin Featherstone
- Editorial & Methods Department, Cochrane, St Albans House, 57-59 Haymarket, London, SW1Y 4QX, UK
| | - Chris Mavergames
- Informatics & Technology Services, Cochrane, St Albans House, 57-59 Haymarket, London, SW1Y 4QX, UK
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Thomas J, McDonald S, Noel-Storr A, Shemilt I, Elliott J, Mavergames C, Marshall IJ. Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews. J Clin Epidemiol 2021; 133:140-151. [PMID: 33171275 PMCID: PMC8168828 DOI: 10.1016/j.jclinepi.2020.11.003] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 10/13/2020] [Accepted: 11/03/2020] [Indexed: 12/29/2022]
Abstract
OBJECTIVES This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. METHODS A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the "Cochrane RCT Classifier"), with the algorithm trained using a data set of title-abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. RESULTS The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98-0.99) and precision of 0.08 (95% confidence interval 0.06-0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. CONCLUSIONS The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production.
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Affiliation(s)
- James Thomas
- EPPI-Centre, UCL Social Research Institute, University College London, London, UK.
| | - Steve McDonald
- Cochrane Australia, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Anna Noel-Storr
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Cochrane, London, UK
| | - Ian Shemilt
- EPPI-Centre, UCL Social Research Institute, University College London, London, UK
| | - Julian Elliott
- Department of Infectious Diseases, Monash University and Alfred Hospital, Melbourne, Australia
| | | | - Iain J Marshall
- School of Population Health & Environmental Sciences, Kings College London, London, UK
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Noel-Storr A, Dooley G, Elliott J, Steele E, Shemilt I, Mavergames C, Wisniewski S, McDonald S, Murano M, Glanville J, Foxlee R, Beecher D, Ware J, Thomas J. An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials. J Clin Epidemiol 2021; 133:130-139. [DOI: 10.1016/j.jclinepi.2021.01.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 12/13/2022]
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Mavergames C, Beecher D, Becker LA, Last A, Ali A. Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints. J Law Med Ethics 2019; 47:374-380. [PMID: 31560633 DOI: 10.1177/1073110519876166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.
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Affiliation(s)
- Chris Mavergames
- Chris Mavergames, M.Sc., is the Chief Information Offier, Cochrane Informatics and Technology (IT) Services and a member of the Cochrane Central Executive team. Deirdre Beecher, M.Sc., is a Senior Metadata Specialist, Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. Lorne A. Becker, M.D., is at Cochrane and is an Emeritus Professor in the Department of Family Medicine, SUNY Upstate Medical University, in Syracuse, NY. A. Last works as a Medical Terminology Manager at Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. A. Ali works at the Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team
| | - Deirdre Beecher
- Chris Mavergames, M.Sc., is the Chief Information Offier, Cochrane Informatics and Technology (IT) Services and a member of the Cochrane Central Executive team. Deirdre Beecher, M.Sc., is a Senior Metadata Specialist, Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. Lorne A. Becker, M.D., is at Cochrane and is an Emeritus Professor in the Department of Family Medicine, SUNY Upstate Medical University, in Syracuse, NY. A. Last works as a Medical Terminology Manager at Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. A. Ali works at the Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team
| | - Lorne A Becker
- Chris Mavergames, M.Sc., is the Chief Information Offier, Cochrane Informatics and Technology (IT) Services and a member of the Cochrane Central Executive team. Deirdre Beecher, M.Sc., is a Senior Metadata Specialist, Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. Lorne A. Becker, M.D., is at Cochrane and is an Emeritus Professor in the Department of Family Medicine, SUNY Upstate Medical University, in Syracuse, NY. A. Last works as a Medical Terminology Manager at Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. A. Ali works at the Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team
| | - A Last
- Chris Mavergames, M.Sc., is the Chief Information Offier, Cochrane Informatics and Technology (IT) Services and a member of the Cochrane Central Executive team. Deirdre Beecher, M.Sc., is a Senior Metadata Specialist, Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. Lorne A. Becker, M.D., is at Cochrane and is an Emeritus Professor in the Department of Family Medicine, SUNY Upstate Medical University, in Syracuse, NY. A. Last works as a Medical Terminology Manager at Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. A. Ali works at the Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team
| | - A Ali
- Chris Mavergames, M.Sc., is the Chief Information Offier, Cochrane Informatics and Technology (IT) Services and a member of the Cochrane Central Executive team. Deirdre Beecher, M.Sc., is a Senior Metadata Specialist, Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. Lorne A. Becker, M.D., is at Cochrane and is an Emeritus Professor in the Department of Family Medicine, SUNY Upstate Medical University, in Syracuse, NY. A. Last works as a Medical Terminology Manager at Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team. A. Ali works at the Cochrane Informatics and Technology (IT) Services and is a member of the Cochrane Central Executive team
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6
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Turner T, Steele E, Mavergames C, Elliott J. Facilitating Web-Based Collaboration in Evidence Synthesis (TaskExchange): Development and Analysis. JMIR Res Protoc 2018; 7:e188. [PMID: 30545818 PMCID: PMC6315246 DOI: 10.2196/resprot.9285] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 06/14/2018] [Accepted: 07/11/2018] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The conduct and publication of scientific research are increasingly open and collaborative. There is growing interest in Web-based platforms that can effectively enable global, multidisciplinary scientific teams and foster networks of scientists in areas of shared research interest. Designed to facilitate Web-based collaboration in research evidence synthesis, TaskExchange highlights the potential of these kinds of platforms. OBJECTIVE This paper describes the development, growth, and future of TaskExchange, a Web-based platform facilitating collaboration in research evidence synthesis. METHODS The original purpose of TaskExchange was to create a platform that connected people who needed help with their Cochrane systematic reviews (rigorous syntheses of health research) with people who had the time and expertise to help. The scope of TaskExchange has now been expanded to include other evidence synthesis tasks, including guideline development. The development of TaskExchange was initially undertaken in 5 agile development phases with substantial user engagement. In each phase, software was iteratively deployed as it was developed and tested, enabling close cycles of development and refinement. RESULTS TaskExchange enables users to browse and search tasks and members by keyword or nested filters, post and respond to tasks, sign up to notification emails, and acknowledge the work of TaskExchange members. The pilot platform has been open access since August 2016, has over 2300 members, and has hosted more than 630 tasks, covering a wide range of research synthesis-related tasks. Response rates are consistently over 75%, and user feedback has been positive. CONCLUSIONS TaskExchange demonstrates the potential for new technologies to support Web-based collaboration in health research. Development of a relatively simple platform for peer-to-peer exchange has provided opportunities for systematic reviewers to get their reviews completed more quickly and provides an effective pathway for people to join the global health evidence community.
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Affiliation(s)
- Tari Turner
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Emily Steele
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | | | - Julian Elliott
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,Department of Infectious Diseases, Monash University and Alfred Hospital, Melbourne, Australia
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Dodd S, Clarke M, Becker L, Mavergames C, Fish R, Williamson PR. A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery. J Clin Epidemiol 2017; 96:84-92. [PMID: 29288712 PMCID: PMC5854263 DOI: 10.1016/j.jclinepi.2017.12.020] [Citation(s) in RCA: 290] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 12/13/2017] [Accepted: 12/20/2017] [Indexed: 11/30/2022]
Abstract
Objectives There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery. Study Design and Setting A literature review was carried out to determine existing outcome classification systems, none of which were sufficiently comprehensive or granular for classification of all potential outcomes from clinical trials. A new taxonomy for outcome classification was developed, and as proof of principle, outcomes extracted from all published COS in the COMET database, selected Cochrane reviews, and clinical trial registry entries were classified using this new system. Results Application of this new taxonomy to COS in the COMET database revealed that 274/299 (92%) COS include at least one physiological outcome, whereas only 177 (59%) include at least one measure of impact (global quality of life or some measure of functioning) and only 105 (35%) made reference to adverse events. Conclusions This outcome taxonomy will be used to annotate outcomes included in COS within the COMET database and is currently being piloted for use in Cochrane Reviews within the Cochrane Linked Data Project. Wider implementation of this standard taxonomy in trial and systematic review databases and registries will further promote efficient searching, reporting, and classification of trial outcomes.
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Affiliation(s)
- Susanna Dodd
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GS, UK
| | - Mike Clarke
- School of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health Institute for Health Sciences, Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, UK
| | - Lorne Becker
- Department of Family Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chris Mavergames
- Department of Informatics and Knowledge Management, Cochrane Central Executive, Freiburg, Germany
| | - Rebecca Fish
- Division of Molecular and Clinical Cancer Sciences, University of Manchester, Manchester, UK
| | - Paula R Williamson
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GS, UK.
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Thomas J, Noel-Storr A, Marshall I, Wallace B, McDonald S, Mavergames C, Glasziou P, Shemilt I, Synnot A, Turner T, Elliott J. Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol 2017; 91:31-37. [PMID: 28912003 DOI: 10.1016/j.jclinepi.2017.08.011] [Citation(s) in RCA: 190] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 08/17/2017] [Accepted: 08/17/2017] [Indexed: 12/29/2022]
Abstract
New approaches to evidence synthesis, which use human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities ("crowds") as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full-text reports, extraction of data, and risk of bias assessment. Workflows can be developed in which human effort and machine automation can each enable the other to operate in more effective and efficient ways, offering substantial enhancement to the productivity of systematic reviews. This paper describes and discusses the potential-and limitations-of new ways of undertaking specific tasks in living systematic reviews, identifying areas where these human/machine "technologies" are already in use, and where further research and development is needed. While the context is living systematic reviews, many of these enabling technologies apply equally to standard approaches to systematic reviewing.
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Affiliation(s)
- James Thomas
- EPPI-Centre, Department of Social Science, University College London, 18 Woburn Square, London, WC1H 0NR, UK.
| | - Anna Noel-Storr
- Radcliffe Department of Medicine, University of Oxford, Level 4, Academic Block, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - Iain Marshall
- Primary Care & Public Health Sciences, Kings College, Capital House, 42 Weston Street, London, UK
| | - Byron Wallace
- College of Computer and Information Science, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Steven McDonald
- Cochrane Australia, School of Public Health and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne VIC 3004, Australia
| | | | - Paul Glasziou
- Centre for Research on Evidence Based Practice, Bond University, 14 University Drive (Off Cottesloe Drive), Robina, QLD 4226, Australia
| | - Ian Shemilt
- EPPI-Centre, Department of Social Science, University College London, 18 Woburn Square, London, WC1H 0NR, UK
| | - Anneliese Synnot
- Cochrane Australia, School of Public Health and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne VIC 3004, Australia; Centre for Health Communication and Participation, School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Tari Turner
- Cochrane Australia, School of Public Health and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne VIC 3004, Australia
| | - Julian Elliott
- Cochrane Australia, School of Public Health and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne VIC 3004, Australia; Department of Infectious Diseases, Monash University and Alfred Hospital, 55 Commercial Rd, Melbourne VIC 3004, Australia
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Garner P, Hopewell S, Chandler J, MacLehose H, Schünemann HJ, Akl EA, Beyene J, Chang S, Churchill R, Dearness K, Guyatt G, Lefebvre C, Liles B, Marshall R, Martínez García L, Mavergames C, Nasser M, Qaseem A, Sampson M, Soares-Weiser K, Takwoingi Y, Thabane L, Trivella M, Tugwell P, Welsh E, Wilson EC, Schünemann HJ. When and how to update systematic reviews: consensus and checklist. BMJ 2016; 354:i3507. [PMID: 27443385 PMCID: PMC4955793 DOI: 10.1136/bmj.i3507] [Citation(s) in RCA: 229] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/26/2016] [Indexed: 01/08/2023]
Affiliation(s)
- Paul Garner
- Cochrane Infectious Diseases Group, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
| | - Sally Hopewell
- Oxford Clinical Trials Research Unit, University of Oxford, Oxford, UK
| | - Jackie Chandler
- Cochrane Editorial Unit, Cochrane Central Executive, London, UK
| | | | - Holger J Schünemann
- Department of Clinical Epidemiology and Biostatistics and Department of Medicine, McMaster University, Hamilton, ON, Canada Cochrane GRADEing Methods Group, Ottawa, ON, Canada
| | - Elie A Akl
- Cochrane GRADEing Methods Group, Ottawa, ON, Canada Department of Internal Medicine, American University of Beirut, Beirut, Lebanon
| | - Joseph Beyene
- Department of Mathematics and Statistics, McMaster University
| | - Stephanie Chang
- Evidence-based Practice Center Program, Agency for Healthcare and Research Quality, Rockville, MD, USA
| | - Rachel Churchill
- Centre for Reviews and Dissemination, University of York, York, UK
| | - Karin Dearness
- Cochrane Upper Gastrointestinal and Pancreatic Diseases Group, Hamilton, ON, Canada
| | - Gordon Guyatt
- Department of Clinical Epidemiology and Biostatistics and Department of Medicine, McMaster University, Hamilton, ON, Canada
| | | | - Beth Liles
- Kaiser Permanente National Guideline Program, Portland, OR, USA
| | - Rachel Marshall
- Cochrane Editorial Unit, Cochrane Central Executive, London, UK
| | | | - Chris Mavergames
- Cochrane Informatics and Knowledge Management, Cochrane Central Executive, Freiburg, Germany
| | - Mona Nasser
- Plymouth University Peninsula School of Dentistry, Plymouth, UK
| | - Amir Qaseem
- Department of Clinical Policy, American College of Physicians,Philadelphia, PA, USA Guidelines International Network, Pitlochry, UK
| | | | | | - Yemisi Takwoingi
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Lehana Thabane
- Department of Clinical Epidemiology and Biostatistics and Department of Medicine, McMaster University, Hamilton, ON, Canada Biostatistics Unit, Centre for Evaluation, McMaster University, Hamilton, ON, Canada
| | | | | | - Emma Welsh
- Cochrane Airways Group, Population Health Research Institute, St George's, University of London, London, UK
| | - Ed C Wilson
- Cambridge Centre for Health Services Research, University of Cambridge, Cambridge, UK
| | - Holger J Schünemann
- Department of Clinical Epidemiology and Biostatistics and Department of Medicine, McMaster University, Hamilton, ON, Canada Cochrane GRADEing Methods Group, Ottawa, ON, Canada
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Slaughter L, Berntsen CF, Brandt L, Mavergames C. Enabling Living Systematic Reviews and Clinical Guidelines through Semantic Technologies. ACTA ACUST UNITED AC 2015. [DOI: 10.1045/january2015-slaughter] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Abstract
The current difficulties in keeping systematic reviews up to date leads to considerable inaccuracy, hampering the translation of knowledge into action. Incremental advances in conventional review updating are unlikely to lead to substantial improvements in review currency. A new approach is needed. We propose living systematic review as a contribution to evidence synthesis that combines currency with rigour to enhance the accuracy and utility of health evidence. Living systematic reviews are high quality, up-to-date online summaries of health research, updated as new research becomes available, and enabled by improved production efficiency and adherence to the norms of scholarly communication. Together with innovations in primary research reporting and the creation and use of evidence in health systems, living systematic review contributes to an emerging evidence ecosystem.
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Affiliation(s)
- Julian H. Elliott
- Department of Infectious Diseases, Alfred Hospital and Monash University, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- * E-mail:
| | - Tari Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- World Vision Australia, Melbourne, Australia
| | - Ornella Clavisi
- National Trauma Research Institute, Alfred Hospital, Melbourne, Australia
| | - James Thomas
- EPPI-Centre, Institute of Education, University of London, London, England
| | - Julian P. T. Higgins
- School of Social and Community Medicine, University of Bristol, Bristol, England
- Centre for Reviews and Dissemination, University of York, York, England
| | - Chris Mavergames
- Informatics and Knowledge Management Department, The Cochrane Collaboration, Freiburg, Germany
| | - Russell L. Gruen
- National Trauma Research Institute, Alfred Hospital, Melbourne, Australia
- Department of Surgery, Monash University, Melbourne, Australia
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Elliott J, Sim I, Thomas J, Owens N, Dooley G, Riis J, Wallace B, Thomas J, Noel-Storr A, Rada G, Struthers C, Howe T, MacLehose H, Brandt L, Kunnamo I, Mavergames C. #CochraneTech: technology and the future of systematic reviews. Cochrane Database Syst Rev 2014; 2014:ED000091. [PMID: 25288182 PMCID: PMC10845870 DOI: 10.1002/14651858.ed000091] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a respiratory disease that causes progressive symptoms of breathlessness, cough and mucus build-up. It is the fourth or fifth most common cause of death worldwide and is associated with significant healthcare costs.Inhaled long-acting beta2-agonists (LABAs) are widely prescribed to manage the symptoms of COPD when short-acting agents alone are no longer sufficient. Twice-daily treatment with an inhaled LABA is aimed at relieving symptoms, improving exercise tolerance and quality of life, slowing decline and even improving lung function and preventing and treating exacerbations. OBJECTIVES To assess the effects of twice-daily long-acting beta2-agonists compared with placebo for patients with COPD on the basis of clinically important endpoints, primarily quality of life and COPD exacerbations. SEARCH METHODS We searched the Cochrane Airways Group trials register, ClinicalTrials.gov and manufacturers' websites in June 2013. SELECTION CRITERIA Parallel, randomised controlled trials (RCTs) recruiting populations of patients with chronic obstructive pulmonary disease. Studies were required to be at least 12 weeks in duration and designed to assess the safety and efficacy of a long-acting beta2-agonist against placebo. DATA COLLECTION AND ANALYSIS Data and characteristics were extracted independently by two review authors, and each study was assessed for potential sources of bias. Data for all outcomes were pooled and subgrouped by LABA agent (formoterol 12 μg, formoterol 24 μg and salmeterol 50 μg) and then were separately analysed by LABA agent and subgrouped by trial duration. Sensitivity analyses were conducted for the proportion of participants taking inhaled corticosteroids and for studies with high or uneven rates of attrition. MAIN RESULTS Twenty-six RCTs met the inclusion criteria, randomly assigning 14,939 people with COPD to receive twice-daily LABA or placebo. Study duration ranged from three months to three years; the median duration was six months. Participants were more often male with moderate to severe symptoms at randomisation; mean forced expiratory volume in 1 second (FEV1) was between 33% and 55% predicted normal in the studies, and mean St George's Respiratory Questionnaire score (SGRQ) ranged from 44 to 55 when reported.Moderate-quality evidence showed that LABA treatment improved quality of life on the SGRQ (mean difference (MD) -2.32, 95% confidence interval (CI) -3.09 to -1.54; I(2) = 50%; 17 trials including 11,397 people) and reduced the number of exacerbations requiring hospitalisation (odds ratio (OR) 0.73, 95% CI 0.56 to 0.95; I(2) = 10%; seven trials including 3804 people). In absolute terms, 18 fewer people per 1000 were hospitalised as the result of an exacerbation while receiving LABA therapy over a weighted mean of 7 months (95% CI 3 to 31 fewer). Scores were also improved on the Chronic Respiratory Disease Questionnaire (CRQ), and more people receiving LABA treatment showed clinically important improvement of at least four points on the SGRQ.The number of people who had exacerbations requiring a course of oral steroids or antibiotics was also lower among those taking LABA (52 fewer per 1000 treated over 8 months; 95% CI 24 to 78 fewer, moderate quality evidence).Mortality was low, and combined findings of all studies showed that LABA therapy did not significantly affect mortality (OR 0.90, 95% CI 0.75 to 1.08; I(2) = 21%; 23 trials including 14,079 people, moderate quality evidence). LABA therapy did not affect the rate of serious adverse events (OR 0.97, 95% CI 0.83 to 1.14; I(2) = 34%, moderate quality evidence), although there was significant unexplained heterogeneity, especially between the two formoterol doses.LABA therapy improved predose FEV1 by 73 mL more than placebo (95% CI 48 to 98; I(2) = 71%, low quality evidence), and people were more likely to withdraw from placebo than from LABA therapy (OR 0.74, 95% CI 0.69 to 0.80; I(2) = 0%). Higher rates of withdrawal in the placebo arm may reduce our confidence in some results, but the disparity is more likely to reduce the magnitude of difference between LABA and placebo than inflate the true effect; removing studies at highest risk of bias on the basis of high and unbalanced attrition did not change conclusions for the primary outcomes. AUTHORS' CONCLUSIONS Moderate-quality evidence from 26 studies showed that inhaled long-acting beta2-agonists are effective over the medium and long term for patients with moderate to severe COPD. Their use is associated with improved quality of life and reduced exacerbations, including those requiring hospitalisation. Overall, findings showed that inhaled LABAs did not significantly reduce mortality or serious adverse events.
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Affiliation(s)
- Kayleigh M Kew
- Population Health Sciences and Education, St George's, University of London, Cranmer Terrace, London, UK, SW17 0RE
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Elliott JH, Mavergames C, Becker L, Meerpohl J, Thomas J, Gruen R, Tovey D. The efficient production of high quality evidence reviews is important for the public good. BMJ 2013; 346:f846. [PMID: 23407729 DOI: 10.1136/bmj.f846] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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15
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Diener MK, Wolff RF, von Elm E, Rahbari NN, Mavergames C, Knaebel HP, Seiler CM, Antes G. Can decision making in general surgery be based on evidence? An empirical study of Cochrane Reviews. Surgery 2009; 146:444-61. [DOI: 10.1016/j.surg.2009.02.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2008] [Accepted: 02/20/2009] [Indexed: 10/20/2022]
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