High-Performance Information Search Filters for CKD Content in PubMed, Ovid MEDLINE, and EMBASE
| Autor: | Amit X. Garg, Gihad Nesrallah, Nancy L. Wilczynski, Brenda R. Hemmelgarn, Adeera Levin, R. Brian Haynes, Arthur V. Iansavichus, Ainslie M. Hildebrand, Karen Tu, Danielle M. Nash |
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| Rok vydání: | 2015 |
| Předmět: |
medicine.medical_specialty
Ovid medline 030232 urology & nephrology MEDLINE urologic and male genital diseases Health informatics Terminology Medical Subject Headings 03 medical and health sciences 0302 clinical medicine Terminology as Topic medicine Humans Relevance (information retrieval) Medical physics 030212 general & internal medicine Renal Insufficiency Chronic Publishing Information Dissemination business.industry Reproducibility of Results Reference Standards medicine.disease Databases Bibliographic 3. Good health Search Engine Bibliographic database Index (publishing) Nephrology Periodicals as Topic business Kidney disease |
| Zdroj: | American Journal of Kidney Diseases. 65:26-32 |
| ISSN: | 0272-6386 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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