This Article Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ingui, B. J. Articles by Rogers, M. A. M. Search for Related Content PubMed PubMed Citation Articles by Ingui, B. J. Articles by Rogers, M. A. M.

Searching for Clinical Prediction Rules in MEDLINE

Affiliation of the authors: Upstate Medical University, Syracuse, New York.

Correspondence and reprints: Bette Jean Ingui, MLS, Senior Assistant Librarian, Health Sciences Library, Upstate Medical University, State University of New York, 766 Irving Avenue, Syracuse, NY 13210; e-mail: <inguiB{at}upstate.edu>.

Received for publication: 01/11/01; accepted for publication: 03/14/01.

Objectives: Clinical prediction rules have been advocated as a possible mechanism to enhance clinical judgment in diagnostic, therapeutic, and prognostic assessment. Despite renewed interest in the their use, inconsistent terminology makes them difficult to index and retrieve by computerized search systems. No validated approaches to locating clinical prediction rules appear in the literature. The objective of this study was to derive and validate an optimal search filter for retrieving clinical prediction rules, using the National Library of Medicine's MEDLINE database.

Design: A comparative, retrospective analysis was conducted. The "gold standard" was established by a manual search of all articles from select print journals for the years 1991 through 1998, which identified articles covering various aspects of clinical prediction rules such as derivation, validation, and evaluation. Search filters were derived, from the articles in the July through December issues of the journals (derivation set), by analyzing the textwords (words in the title and abstract) and the medical subject heading (from the MeSH Thesaurus) used to index each article. The accuracy of these filters in retrieving clinical prediction rules was then assessed using articles in the January through June issues (validation set).

Measurements: The sensitivity, specificity, positive predictive value, and positive likelihood ratio of several different search filters were measured.

Results: The filter "predict$ OR clinical$ OR outcome$ OR risk$" retrieved 98 percent of clinical prediction rules. Four filters, such as "predict$ OR validat$ OR rule$ OR predictive value of tests," had both sensitivity and specificity above 90 percent. The top-performing filter for positive predictive value and positive likelihood ratio in the validation set was "predict$.ti. AND rule$."

Conclusions: Several filters with high retrieval value were found. Depending on the goals and time constraints of the searcher, one of these filters could be used.

This article has been cited by other articles:

				K. Lam, T. Parkin, C. Riggs, and K. Morgan Use of free text clinical records in identifying syndromes and analysing health data Vet Rec., October 20, 2007; 161(16): 547 - 551. [Abstract] [Full Text] [PDF]

				B. Smith and J. A Cleland Is radiologic examination necessary for a 9-year-old girl with a knee injury? Physical Therapy, November 1, 2004; 84(11): 1092 - 1100. [Full Text] [PDF]