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

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Background: Tools to enhance physician searches of Medline and other bibliographic databases have potential to improve the application of new knowledge in patient care. This is particularly true for articles about glomerular disease, which are published across multiple disciplines and are often difficult to track down. Our objective was to develop and test search filters for PubMed, Ovid Medline, and Embase that allow physicians to search within a subset of the database to retrieve articles relevant to glomerular disease. Methods. We used a diagnostic test assessment framework with development and validation phases. We read a total of 22,992 full text articles for relevance and assigned them to the development or validation set to define the reference standard. We then used combinations of search terms to develop 997,298 unique glomerular disease filters. Outcome measures for each filter included sensitivity, specificity, precision, and accuracy. We selected optimal sensitive and specific search filters for each database and applied them to the validation set to test performance. Results: High performance filters achieved at least 93.8% sensitivity and specificity in the development set. Filters optimized for sensitivity reached at least 96.7% sensitivity and filters optimized for specificity reached at least 98.4% specificity. Performance of these filters was consistent in the validation set and similar among all three databases. Conclusions: PubMed, Ovid Medline, and Embase can be filtered for articles relevant to glomerular disease in a reliable manner. These filters can now be used to facilitate physician searching. © 2012 Hildebrand et al.; licensee BioMed Central Ltd.




Hildebrand, A. M., Iansavichus, A. V., Lee, C. W., Haynes, R. B., Wilczynski, N. L., McKibbon, K. A., … Garg, A. X. (2012). Glomerular disease search filters for Pubmed, Ovid Medline, and Embase: A development and validation study. BMC Medical Informatics and Decision Making, 12(1).

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