Published 18 September 2009, doi:10.1136/bmj.b3435
Cite this as: BMJ 2009;339:b3435

Research

Filtering Medline for a clinical discipline: diagnostic test assessment framework

Amit X Garg, associate professor1,2,3, 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

1 Division of Nephrology, University of Western Ontario, London, ON, Canada N6A 5C1, 2 Department of Epidemiology and Biostatistics, University of Western Ontario, 3 Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada L8N 3Z5, 4 Department 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 amit.garg{at}lhsc.on.ca

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.


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