The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Searching for Clinical Prediction Rules in MEDLINE
Open accessAbstract
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.
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Cites
Clinical Prediction Rules: Applications and Methodological Standards
Wasson, J. H., Sox, H. C., Neff, R. K., & Goldman, L. (1985) · New England Journal of Medicine · doi:10.1056/nejm198509263131306 · Cited by 1,357
Cited by
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-1377 · Cited by 1,669
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001381 · Cited by 1,508
Goldstein, B. A., Navar, A. M., Pencina, M. J., & Ioannidis, J. P. A. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw042 · Cited by 936
Metadata
- Authors
- Ingui, B. J., & Rogers, M. A. M.
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-07-01
- Volume
- 8
- Issue
- 4
- Pages
- 391-397
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- OpenAlex
- W2172206840
- Cited by
- 184
- References
- 1
- FWCI
- 5.49
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Ingui, B. J., & Rogers, M. A. M. (2001). Searching for Clinical Prediction Rules in MEDLINE. Journal of the American Medical Informatics Association. https://doi.org/10.1136/jamia.2001.0080391
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.