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Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance

BACKGROUND: Information overload, increasing time constraints, and inappropriate search strategies complicate the detection of clinical practice guidelines (CPGs). The aim of this study was to provide clinicians with recommendations for search strategies to efficiently identify relevant CPGs in SUMS...

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Autores principales: Haase, Andrea, Follmann, Markus, Skipka, Guido, Kirchner, Hanna
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1925105/
https://www.ncbi.nlm.nih.gov/pubmed/17603909
http://dx.doi.org/10.1186/1471-2288-7-28
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author Haase, Andrea
Follmann, Markus
Skipka, Guido
Kirchner, Hanna
author_facet Haase, Andrea
Follmann, Markus
Skipka, Guido
Kirchner, Hanna
author_sort Haase, Andrea
collection PubMed
description BACKGROUND: Information overload, increasing time constraints, and inappropriate search strategies complicate the detection of clinical practice guidelines (CPGs). The aim of this study was to provide clinicians with recommendations for search strategies to efficiently identify relevant CPGs in SUMSearch and Google Scholar. METHODS: We compared the retrieval efficiency (retrieval performance) of search strategies to identify CPGs in SUMSearch and Google Scholar. For this purpose, a two-term GLAD (GuideLine And Disease) strategy was developed, combining a defined CPG term with a specific disease term (MeSH term). We used three different CPG terms and nine MeSH terms for nine selected diseases to identify the most efficient GLAD strategy for each search engine. The retrievals for the nine diseases were pooled. To compare GLAD strategies, we used a manual review of all retrievals as a reference standard. The CPGs detected had to fulfil predefined criteria, e.g., the inclusion of therapeutic recommendations. Retrieval performance was evaluated by calculating so-called diagnostic parameters (sensitivity, specificity, and "Number Needed to Read" [NNR]) for search strategies. RESULTS: The search yielded a total of 2830 retrievals; 987 (34.9%) in Google Scholar and 1843 (65.1%) in SUMSearch. Altogether, we found 119 unique and relevant guidelines for nine diseases (reference standard). Overall, the GLAD strategies showed a better retrieval performance in SUMSearch than in Google Scholar. The performance pattern between search engines was similar: search strategies including the term "guideline" yielded the highest sensitivity (SUMSearch: 81.5%; Google Scholar: 31.9%), and search strategies including the term "practice guideline" yielded the highest specificity (SUMSearch: 89.5%; Google Scholar: 95.7%), and the lowest NNR (SUMSearch: 7.0; Google Scholar: 9.3). CONCLUSION: SUMSearch is a useful tool to swiftly gain an overview of available CPGs. Its retrieval performance is superior to that of Google Scholar, where a search is more time consuming, as substantially more retrievals have to be reviewed to detect one relevant CPG. In both search engines, the CPG term "guideline" should be used to obtain a comprehensive overview of CPGs, and the term "practice guideline" should be used if a less time consuming approach for the detection of CPGs is desired.
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spelling pubmed-19251052007-07-20 Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance Haase, Andrea Follmann, Markus Skipka, Guido Kirchner, Hanna BMC Med Res Methodol Research Article BACKGROUND: Information overload, increasing time constraints, and inappropriate search strategies complicate the detection of clinical practice guidelines (CPGs). The aim of this study was to provide clinicians with recommendations for search strategies to efficiently identify relevant CPGs in SUMSearch and Google Scholar. METHODS: We compared the retrieval efficiency (retrieval performance) of search strategies to identify CPGs in SUMSearch and Google Scholar. For this purpose, a two-term GLAD (GuideLine And Disease) strategy was developed, combining a defined CPG term with a specific disease term (MeSH term). We used three different CPG terms and nine MeSH terms for nine selected diseases to identify the most efficient GLAD strategy for each search engine. The retrievals for the nine diseases were pooled. To compare GLAD strategies, we used a manual review of all retrievals as a reference standard. The CPGs detected had to fulfil predefined criteria, e.g., the inclusion of therapeutic recommendations. Retrieval performance was evaluated by calculating so-called diagnostic parameters (sensitivity, specificity, and "Number Needed to Read" [NNR]) for search strategies. RESULTS: The search yielded a total of 2830 retrievals; 987 (34.9%) in Google Scholar and 1843 (65.1%) in SUMSearch. Altogether, we found 119 unique and relevant guidelines for nine diseases (reference standard). Overall, the GLAD strategies showed a better retrieval performance in SUMSearch than in Google Scholar. The performance pattern between search engines was similar: search strategies including the term "guideline" yielded the highest sensitivity (SUMSearch: 81.5%; Google Scholar: 31.9%), and search strategies including the term "practice guideline" yielded the highest specificity (SUMSearch: 89.5%; Google Scholar: 95.7%), and the lowest NNR (SUMSearch: 7.0; Google Scholar: 9.3). CONCLUSION: SUMSearch is a useful tool to swiftly gain an overview of available CPGs. Its retrieval performance is superior to that of Google Scholar, where a search is more time consuming, as substantially more retrievals have to be reviewed to detect one relevant CPG. In both search engines, the CPG term "guideline" should be used to obtain a comprehensive overview of CPGs, and the term "practice guideline" should be used if a less time consuming approach for the detection of CPGs is desired. BioMed Central 2007-06-30 /pmc/articles/PMC1925105/ /pubmed/17603909 http://dx.doi.org/10.1186/1471-2288-7-28 Text en Copyright © 2007 Haase 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
Haase, Andrea
Follmann, Markus
Skipka, Guido
Kirchner, Hanna
Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance
title Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance
title_full Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance
title_fullStr Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance
title_full_unstemmed Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance
title_short Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance
title_sort developing search strategies for clinical practice guidelines in sumsearch and google scholar and assessing their retrieval performance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1925105/
https://www.ncbi.nlm.nih.gov/pubmed/17603909
http://dx.doi.org/10.1186/1471-2288-7-28
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