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

@article{citeulike:1426104, abstract = {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.}, author = {Haase, Andrea and Follmann, Markus and Skipka, Guido and Kirchner, Hanna}, citeulike-article-id = {1426104}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2288-7-28}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17603909}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17603909}, day = {30}, doi = {10.1186/1471-2288-7-28}, issn = {1471-2288}, journal = {BMC Medical Research Methodology}, keywords = {database-evaluation, google-scholar}, month = {June}, number = {1}, pages = {28+}, posted-at = {2008-04-22 07:18:40}, priority = {5}, title = {Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance}, url = {http://dx.doi.org/10.1186/1471-2288-7-28}, volume = {7}, year = {2007} }

TY - JOUR ID - citeulike:1426104 L3 - citeulike-article-id:1426104 N2 - 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. IS - 1 JF - BMC Medical Research Methodology SN - 1471-2288 TI - Developing search strategies for clinical practice guidelines in SUMSearch and Google Scholar and assessing their retrieval performance VL - 7 SP - 28 KW - database-evaluation KW - google-scholar AU - Haase, Andrea AU - Follmann, Markus AU - Skipka, Guido AU - Kirchner, Hanna PY - 2007/06/30/ UR - http://dx.doi.org/10.1186/1471-2288-7-28 ER -