- Amit X Garg, associate professor123,
- Arthur V Iansavichus, information specialist 1,
- Nancy L Wilczynski, assistant professor 3,
- Monika Kastner, PhD student 4,
- Leslie A Baier, research assistant 3,
- Salimah Z Shariff, PhD student 1,
- Faisal Rehman, assistant professor 1,
- Matthew Weir, research fellow 1,
- K Ann McKibbon, associate professor 3,
- R Brian Haynes, professor 3
- 1Division of Nephrology, University of Western Ontario, London, ON, Canada N6A 5C1
- 2Department of Epidemiology and Biostatistics, University of Western Ontario
- 3Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada L8N 3Z5
- 4Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada M5T 3M6
- Correspondence to: A Garg, London Kidney Clinical Research Unit, Room ELL-101, Westminster, London Health Sciences Centre, 800 Commissioners Road East, London, ON, Canada N6A 4G5
- Accepted 30 March 2009
Objective To develop and test a Medline filter that allows clinicians to search for articles within a clinical discipline, rather than searching the entire Medline database.
Design Diagnostic test assessment framework with development and validation phases.
Setting Sample of 4657 articles published in 2006 from 40 journals.
Reviews Each article was manually reviewed, and 19.8% contained information relevant to the discipline of nephrology. The performance of 1 155 087 unique renal filters was compared with the manual review.
Main outcome measures Sensitivity, specificity, precision, and accuracy of each filter.
Results The best renal filters combined two to 14 terms or phrases and included the terms “kidney” with multiple endings (that is, truncation), “renal replacement therapy”, “renal dialysis”, “kidney function tests”, “renal”, “nephr” truncated, “glomerul” truncated, and “proteinuria”. These filters achieved peak sensitivities of 97.8% and specificities of 98.5%. Performance of filters remained excellent in the validation phase.
Conclusions Medline can be filtered for the discipline of nephrology in a reliable manner. Storing these high performance renal filters in PubMed could help clinicians with their everyday searching. Filters can also be developed for other clinical disciplines by using similar methods.
We thank other members of our research team: Nicholas Hobson and Chris Cotoi, who did the computer programming, and Robert Yang who helped to develop the criteria used to assess renal information.
Contributors: AXG, AVI, NLW, KAM, and RBH conceived the study. AVI compiled articles and managed data. AXG, AVI, MK, and LAB rated the articles for renal relevance. NLW and RBH supervised the computer programming. All authors had full access to data and aided the interpretation. SZS organised the clinicians’ searches. AXG drafted the manuscript, and all authors revised it. AXG is the guarantor.
Funding: This study was funded by the Kidney Foundation of Canada. AXG was supported by a clinician scientist award from the Canadian Institutes of Health Research. The researchers were independent of the funders. The funders had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Competing interests: None declared.
Ethics approval: The study was approved by the regional ethics board of the University of Western Ontario. The five clinician searchers provided informed consent for study participation.
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