期刊名称：Evidence Based Library and Information Practice
印刷版ISSN：1715-720X
电子版ISSN：1715-720X
出版年度：2019
卷号：14
期号：1
页码：65-67
DOI：10.18438/eblip29537
出版社：University Of Alberta
摘要：Objective – “To develop and validate search filters for MEDLINE and Embase for the adverse effects of surgical interventions” (p.121). Design – From a universe of systematic reviews, the authors created “an unselected cohort…where relevant articles are not chosen because of the presence of adverse effects terms” (p.123). The studies referenced in the cohort reviews were extracted to create an overall citation set. From this, three equal-sized sets of studies were created by random selection, and used for: development of a filter (identifying search terms); evaluation of the filter (testing how well it worked); and validation of the filter (assessing how well it retrieved relevant studies). Setting – Systematic reviews of adverse effects from the Database of s of Reviews of Effects (DARE), published in 2014. Subjects – 358 studies derived from the references of 19 systematic reviews (352 available in MEDLINE, 348 available in Embase). Methods – Word and phrase frequency analysis was performed on the development set of articles to identify a list of terms, starting with the term creating the highest recall from titles and s of articles, and continuing until adding new search terms produced no more new records recalled. The search strategy thus developed was then tested on the evaluation set of articles. In this case, using the strategy recalled all of the articles which could be obtained using generic search terms; however, adding specific search terms (such as the MeSH term “surgical site infection”) improved recall. Finally, the strategy incorporating both generic and specific search terms for adverse effects was used on the validation set of articles. Search strategies used are included in the article, as is a list in the discussion section of MeSH and Embase indexing terms specific to or suggesting adverse effects. Main Results – “In each case the addition of specific adverse effects terms could have improved the recall of the searches” (p. 127). This was true for all six cases (development, evaluation and validation study sets, for each of MEDLINE and Embase) in which specific terms were added to searches using generic terms, and recall percentages compared. Conclusion – While no filter can deliver 100% of items in a given standard set of studies on adverse effects (since title and fields may not contain any indication of relevance to the topic), adding specific adverse effects terms to generic ones while developing filters is shown to improve recall for surgery-related adverse effects (similarly to drug-related adverse effects). The use of filters requires user engagement and critical analysis; at the same time, deploying well-constructed filters can have many benefits, including: helping users, especially clinicians, get a search started; managing a large and unwieldy set of citations retrieved; and to suggest new search strategies.
其他摘要：Objective – “To develop and validate search filters for MEDLINE and Embase for the adverse effects of surgical interventions” (p.121). Design – From a universe of systematic reviews, the authors created “an unselected cohort…where relevant articles are not chosen because of the presence of adverse effects terms” (p.123). The studies referenced in the cohort reviews were extracted to create an overall citation set. From this, three equal-sized sets of studies were created by random selection, and used for: development of a filter (identifying search terms); evaluation of the filter (testing how well it worked); and validation of the filter (assessing how well it retrieved relevant studies). Setting – Systematic reviews of adverse effects from the Database of Abstracts of Reviews of Effects (DARE), published in 2014. Subjects – 358 studies derived from the references of 19 systematic reviews (352 available in MEDLINE, 348 available in Embase). Methods – Word and phrase frequency analysis was performed on the development set of articles to identify a list of terms, starting with the term creating the highest recall from titles and abstracts of articles, and continuing until adding new search terms produced no more new records recalled. The search strategy thus developed was then tested on the evaluation set of articles. In this case, using the strategy recalled all of the articles which could be obtained using generic search terms; however, adding specific search terms (such as the MeSH term “surgical site infection”) improved recall. Finally, the strategy incorporating both generic and specific search terms for adverse effects was used on the validation set of articles. Search strategies used are included in the article, as is a list in the discussion section of MeSH and Embase indexing terms specific to or suggesting adverse effects. Main Results – “In each case the addition of specific adverse effects terms could have improved the recall of the searches” (p. 127). This was true for all six cases (development, evaluation and validation study sets, for each of MEDLINE and Embase) in which specific terms were added to searches using generic terms, and recall percentages compared. Conclusion – While no filter can deliver 100% of items in a given standard set of studies on adverse effects (since title and fields may not contain any indication of relevance to the topic), adding specific adverse effects terms to generic ones while developing filters is shown to improve recall for surgery-related adverse effects (similarly to drug-related adverse effects). The use of filters requires user engagement and critical analysis; at the same time, deploying well-constructed filters can have many benefits, including: helping users, especially clinicians, get a search started; managing a large and unwieldy set of citations retrieved; and to suggest new search strategies.
