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Development and validation of filters for the retrieval of studies of clinical examination from medline

Shaikh, N and Badgett, RG and Pi, M and Wilczynski, NL and McKibbon, KA and Ketchum, AM and Haynes, RB (2011) Development and validation of filters for the retrieval of studies of clinical examination from medline. Journal of Medical Internet Research, 13 (4).

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Abstract

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. © Nader Shaikh, Robert G. Badgett, Mina Pi, Nancy L. Wilczynski, K. Ann McKibbon, Andrea M. Ketchum, R. Brian Haynes.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Shaikh, Nnas67@pitt.eduNAS67
Badgett, RG
Pi, M
Wilczynski, NL
McKibbon, KA
Ketchum, AMketchum@pitt.eduKETCHUM0000-0002-4384-1294
Haynes, RB
Date: 1 January 2011
Date Type: Publication
Journal or Publication Title: Journal of Medical Internet Research
Volume: 13
Number: 4
DOI or Unique Handle: 10.2196/jmir.1826
Schools and Programs: School of Medicine > Pediatrics
Refereed: Yes
Related URLs:
MeSH Headings: Databases, Factual; Diagnosis; Diagnostic Techniques and Procedures; Humans; MEDLINE
Other ID: NLM PMC3222198
PubMed Central ID: PMC3222198
PubMed ID: 22011384
Date Deposited: 11 Sep 2012 21:48
Last Modified: 22 Dec 2020 19:56
URI: http://d-scholarship.pitt.edu/id/eprint/14041

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