TY  - JOUR
A1  - Shaikh, Nader
A1  - Badgett, Robert G.
A1  - Pi, Mina
A1  - Wilczynski, Nancy L.
A1  - McKibbon, K.Ann
A1  - Ketchum, Andrea M.
A1  - Haynes, R.Brian
T1  - Development and validation of filters for the retrieval of studies of clinical examination from Medline.
JO  - Journal of medical Internet research
Y1  - 2011/10/19
VL  - 13
IS  - 4
SP  - e82
SN  - 1438-8871
AD  - University of Pittsburgh School of Medicine, General Academic Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA. nader.shaikh@chp.edu
N2  - BACKGROUND: Efficiently finding clinical examination studies--studies that quantify the value of symptoms and signs in the diagnosis of disease-is becoming increasingly difficult. . Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests. . OBJECTIVE: The objective was to develop filters for retrieving clinical examination studies from Medline. . METHODS: We developed filters in a training dataset and validated them in a testing database. . We created the training database by hand searching 161 journals (n = 52,636 studies). . We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. . To identify best combinations of these search terms, we used recursive partitioning. . The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). . We also examined the impact of examining reference lists of included articles on recall. . RESULTS: In the training database, the single-term filters with the highest recall (95%) and the highest precision (8.4%) were diagnosis[subheading] and "medical history taking"[MeSH], respectively. . The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100% and a precision of 89% in the training database. . In the testing database, the Haynes-2004-Sensitive filter (recall 98%, precision 0.13%) and the RP filter (recall 89%, precision 0.52%) showed the best performance. . The recall of these two filters increased to 99% and 94% respectively with review of the reference lists of the included articles. . CONCLUSIONS: Recursive partitioning appears to be a useful method of developing search filters. . The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. . Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine.
ID  - 22011384.001
ER  -