Glomerular disease search filters for Pubmed, Ovid Medline, and Embase: a development and validation study

Topic search filters: a systematic scoping review

BACKGROUND

Searching for topics within large biomedical databases can be challenging, especially when topics are complex, diffuse, emerging or lack definitional clarity. Experimentally derived topic search filters offer a reliable solution to effective retrieval; however, their number and range of subject foci remain unknown.

OBJECTIVES

This systematic scoping review aims to identify and describe available experimentally developed topic search filters.

METHODS

Reports on topic search filter development (1990-) were sought using grey literature sources and 15 databases. Reports describing the conception and prospective development of a database-specific topic search and including an objectively measured estimate of its performance ('sensitivity') were included.

RESULTS

Fifty-four reports met inclusion criteria. Data were extracted and thematically synthesised to describe the characteristics of 58 topic search filters.

DISCUSSION

Topic search filters are proliferating and cover a wide range of subjects. Filter reports, however, often lack clear definitions of concepts and topic scope to guide users. Without standardised terminology, filters are challenging to find. Information specialists may benefit from a centralised topic filter repository and appraisal checklists to facilitate quality assessment.

CONCLUSION

Findings will help information specialists identify existing topic search filters and assist filter developers to build on current knowledge in the field.

Development and validation of a MEDLINE search filter/hedge for degenerative cervical myelopathy

Background

Degenerative cervical myelopathy (DCM) is a common condition with many unmet clinical needs. Pooled analysis of studies is an important tool for advancing medical understanding. This process starts with a systematic search of the literature. Identification of studies in DCM is challenged by a number of factors, including non-specific terminology and index terms. Search filters or HEDGEs, are search strings developed and validated to optimise medical literature searches. We aimed to develop a search filter for DCM for the MEDLINE database.

Methods

The diagnostic test assessment framework of a “development dataset” and seperate “validation dataset” was used. The development dataset was formed by hand searching four leading spinal journals (Spine, Journal of Neurosurgery Spine, Spinal Cord and Journal of Spinal Disorders and Techniques) in 2005 and 2010. The search filter was initially developed focusing on sensitivity and subsequently refined using NOT functions to improve specificity. One validation dataset was formed from DCM narrative and systematic review articles and the second, articles published in April of 1989, 1993, 1997, 2001, 2005, 2009, 2013 and 2017 retrieved via the search MeSH term ‘Spine’. Metrics of sensitivity, specificity, precision and accuracy were used to test performance.

Results

Hand searching identified 77/1094 relevant articles for 2005 and 55/1199 for 2010. We developed a search hedge with 100% sensitivity and a precision of 30 and 29% for the 2005 and 2010 development datasets respectively. For the selected time periods, EXP Spine returned 2113 publications and 30 were considered relevant. The search filter identified all 30 relevant articles, with a specificity of 94% and precision of 20%. Of the 255 references listed in the narrative index reviews, 225 were indexed in MEDLINE and 165 (73%) were relevant articles. All relevant articles were identified and accuracy ranged from 67 to 97% over the three reviews. Of the 42 articles returned from 3 recent systematic reviews, all were identified by the filter.

Conclusions

We have developed a highly sensitive hedge for the research of DCM. Whilst precision is similarly low as other hedges, this search filter can be used as an adjunct for DCM search strategies.

Electronic supplementary material

The online version of this article (10.1186/s12874-018-0529-3) contains supplementary material, which is available to authorized users.

An automated system for retrieving herb-drug interaction related articles from MEDLINE

An automated, user-friendly and accurate system for retrieving herb-drug interaction (HDIs) related articles in MEDLINE can increase the safety of patients, as well as improve the physicians' article retrieving ability regarding speed and experience. Previous studies show that MeSH based queries associated with negative effects of drugs can be customized, resulting in good performance in retrieving relevant information, but no study has focused on the area of herb-drug interactions (HDI). This paper adapted the characteristics of HDI related papers and created a multilayer HDI article searching system. It achieved a sensitivity of 92% at a precision of 93% in a preliminary evaluation. Instead of requiring physicians to conduct PubMed searches directly, this system applies a more user-friendly approach by employing a customized system that enhances PubMed queries, shielding users from having to write queries, dealing with PubMed, or reading many irrelevant articles. The system provides automated processes and outputs target articles based on the input.

Automated Determination of Publications Related to Adverse Drug Reactions in PubMed

Timely dissemination of up-to-date information concerning adverse drug reactions (ADRs) at the point of care can significantly improve medication safety and prevent ADRs. Automated methods for finding relevant articles in MEDLINE which discuss ADRs for specific medications can facilitate decision making at the point of care. Previous work has focused on other types of clinical queries and on retrieval for specific ADRs or drug-ADR pairs, but little work has been published on finding ADR articles for a specific medication. We have developed a method to generate a PubMED query based on MESH, supplementary concepts, and textual terms for a particular medication. Evaluation was performed on a limited sample, resulting in a sensitivity of 90% and precision of 93%. Results demonstrated that this method is highly effective. Future work will integrate this method within an interface aimed at facilitating access to ADR information for specified drugs at the point of care.

Development of a search filter for identifying studies completed in primary care

BACKGROUND

Identifying articles relevant to primary care is challenging for busy clinicians. Setting specific search strategies can be used to help clinicians find pertinent studies in a timely fashion.

OBJECTIVES

To develop search filters for identifying research studies of relevance to primary care in MEDLINE (OvidSP).

METHODS

We conducted a search of MEDLINE (OvidSP) for articles published in five core medical journals at five yearly intervals. We identified a gold standard set of primary care relevant articles which was divided into two subsets. The first subset was used to identify frequently occurring words and phrases through textual analysis. Search filters were developed from these words and phrases and internally validated against records in the second subset. We evaluated the filters performance in a search for articles on two common primary care conditions in MEDLINE (OvidSP).

RESULTS

Of the 12 045 articles retrieved, 9028 records were reviewed, of which 371 articles were relevant to primary care (gold standard). When the search filters generated from textual analysis were internally validated, filter specificity peaked at 99% with 60% sensitivity, 67% precision and 97% accuracy. When evaluated against a set of articles on two common primary care conditions, the best performing combination search filter specificity maximized at 99.7% with sensitivity reaching 15% (precision 90%; accuracy 89%).

CONCLUSION

The best performing combination search filter works well in reducing the number of irrelevant papers retrieved in a MEDLINE (OvidSP) search if a busy clinician needs to focus on research relevant to primary care.

High-performance information search filters for CKD content in PubMed, Ovid MEDLINE, and EMBASE

BACKGROUND

Finding relevant articles in large bibliographic databases such as PubMed, Ovid MEDLINE, and EMBASE to inform care and future research is challenging. Articles relevant to chronic kidney disease (CKD) are particularly difficult to find because they are often published under different terminology and are found across a wide range of journal types.

STUDY DESIGN

We used computer automation within a diagnostic test assessment framework to develop and validate information search filters to identify CKD articles in large bibliographic databases.

SETTING & PARTICIPANTS

22,992 full-text articles in PubMed, Ovid MEDLINE, or EMBASE.

INDEX TEST

1,374,148 unique search filters.

REFERENCE TEST

We established the reference standard of article relevance to CKD by manual review of all full-text articles using prespecified criteria to determine whether each article contained CKD content or not. We then assessed filter performance by calculating sensitivity, specificity, and positive predictive value for the retrieval of CKD articles. Filters with high sensitivity and specificity for the identification of CKD articles in the development phase (two-thirds of the sample) were then retested in the validation phase (remaining one-third of the sample).

RESULTS

We developed and validated high-performance CKD search filters for each bibliographic database. Filters optimized for sensitivity reached at least 99% sensitivity, and filters optimized for specificity reached at least 97% specificity. The filters were complex; for example, one PubMed filter included more than 89 terms used in combination, including "chronic kidney disease," "renal insufficiency," and "renal fibrosis." In proof-of-concept searches, physicians found more articles relevant to the topic of CKD with the use of these filters.

LIMITATIONS

As knowledge of the pathogenesis of CKD grows and definitions change, these filters will need to be updated to incorporate new terminology used to index relevant articles.

CONCLUSIONS

PubMed, Ovid MEDLINE, and EMBASE can be filtered reliably for articles relevant to CKD. These high-performance information filters are now available online and can be used to better identify CKD content in large bibliographic databases.

High-performance information search filters for acute kidney injury content in PubMed, Ovid Medline and Embase

BACKGROUND

We frequently fail to identify articles relevant to the subject of acute kidney injury (AKI) when searching the large bibliographic databases such as PubMed, Ovid Medline or Embase. To address this issue, we used computer automation to create information search filters to better identify articles relevant to AKI in these databases.

METHODS

We first manually reviewed a sample of 22 992 full-text articles and used prespecified criteria to determine whether each article contained AKI content or not. In the development phase (two-thirds of the sample), we developed and tested the performance of >1.3-million unique filters. Filters with high sensitivity and high specificity for the identification of AKI articles were then retested in the validation phase (remaining third of the sample).

RESULTS

We succeeded in developing and validating high-performance AKI search filters for each bibliographic database with sensitivities and specificities in excess of 90%. Filters optimized for sensitivity reached at least 97.2% sensitivity, and filters optimized for specificity reached at least 99.5% specificity. The filters were complex; for example one PubMed filter included >140 terms used in combination, including 'acute kidney injury', 'tubular necrosis', 'azotemia' and 'ischemic injury'. In proof-of-concept searches, physicians found more articles relevant to topics in AKI with the use of the filters.

CONCLUSIONS

PubMed, Ovid Medline and Embase can be filtered for articles relevant to AKI in a reliable manner. These high-performance information filters are now available online and can be used to better identify AKI content in large bibliographic databases.