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Filtering Medline for a clinical discipline: diagnostic test assessment framework

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...

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Detalles Bibliográficos
Autores principales: Garg, Amit X, Iansavichus, Arthur V, Wilczynski, Nancy L, Kastner, Monika, Baier, Leslie A, Shariff, Salimah Z, Rehman, Faisal, Weir, Matthew, McKibbon, K Ann, Haynes, R Brian
Formato: Texto
Lenguaje:English
Publicado: BMJ Publishing Group Ltd. 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2746885/
https://www.ncbi.nlm.nih.gov/pubmed/19767336
http://dx.doi.org/10.1136/bmj.b3435
Descripción
Sumario: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.