Cargando…

Optimal search strategies for identifying sound clinical prediction studies in EMBASE

BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing...

Descripción completa

Detalles Bibliográficos
Autores principales: Holland, Jennifer L, Wilczynski, Nancy L, Haynes, R Brian
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1097733/
https://www.ncbi.nlm.nih.gov/pubmed/15862125
http://dx.doi.org/10.1186/1472-6947-5-11
_version_ 1782123913823649792
author Holland, Jennifer L
Wilczynski, Nancy L
Haynes, R Brian
author_facet Holland, Jennifer L
Wilczynski, Nancy L
Haynes, R Brian
author_sort Holland, Jennifer L
collection PubMed
description BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies.
format Text
id pubmed-1097733
institution National Center for Biotechnology Information
language English
publishDate 2005
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-10977332005-05-12 Optimal search strategies for identifying sound clinical prediction studies in EMBASE Holland, Jennifer L Wilczynski, Nancy L Haynes, R Brian BMC Med Inform Decis Mak Research Article BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies. BioMed Central 2005-04-29 /pmc/articles/PMC1097733/ /pubmed/15862125 http://dx.doi.org/10.1186/1472-6947-5-11 Text en Copyright © 2005 Holland et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Holland, Jennifer L
Wilczynski, Nancy L
Haynes, R Brian
Optimal search strategies for identifying sound clinical prediction studies in EMBASE
title Optimal search strategies for identifying sound clinical prediction studies in EMBASE
title_full Optimal search strategies for identifying sound clinical prediction studies in EMBASE
title_fullStr Optimal search strategies for identifying sound clinical prediction studies in EMBASE
title_full_unstemmed Optimal search strategies for identifying sound clinical prediction studies in EMBASE
title_short Optimal search strategies for identifying sound clinical prediction studies in EMBASE
title_sort optimal search strategies for identifying sound clinical prediction studies in embase
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1097733/
https://www.ncbi.nlm.nih.gov/pubmed/15862125
http://dx.doi.org/10.1186/1472-6947-5-11
work_keys_str_mv AT hollandjenniferl optimalsearchstrategiesforidentifyingsoundclinicalpredictionstudiesinembase
AT wilczynskinancyl optimalsearchstrategiesforidentifyingsoundclinicalpredictionstudiesinembase
AT haynesrbrian optimalsearchstrategiesforidentifyingsoundclinicalpredictionstudiesinembase
AT optimalsearchstrategiesforidentifyingsoundclinicalpredictionstudiesinembase