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The comparative recall of Google Scholar versus PubMed in identical searches for biomedical systematic reviews: a review of searches used in systematic reviews

机译:Google学术搜索与PubMed在生物医学系统评价的相同搜索中的比较召回:系统评价中使用的搜索的回顾

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Background The usefulness of Google Scholar (GS) as a bibliographic database for biomedical systematic review (SR) searching is a subject of current interest and debate in research circles. Recent research has suggested GS might even be used alone in SR searching. This assertion is challenged here by testing whether GS can locate all studies included in 21 previously published SRs. Second, it examines the recall of GS, taking into account the maximum number of items that can be viewed, and tests whether more complete searches created by an information specialist will improve recall compared to the searches used in the 21 published SRs. Methods The authors identified 21 biomedical SRs that had used GS and PubMed as information sources and reported their use of identical, reproducible search strategies in both databases. These search strategies were rerun in GS and PubMed, and analyzed as to their coverage and recall. Efforts were made to improve searches that underperformed in each database. Results GS’ overall coverage was higher than PubMed (98% versus 91%) and overall recall is higher in GS: 80% of the references included in the 21 SRs were returned by the original searches in GS versus 68% in PubMed. Only 72% of the included references could be used as they were listed among the first 1,000 hits (the maximum number shown). Practical precision (the number of included references retrieved in the first 1,000, divided by 1,000) was on average 1.9%, which is only slightly lower than in other published SRs. Improving searches with the lowest recall resulted in an increase in recall from 48% to 66% in GS and, in PubMed, from 60% to 85%. Conclusions Although its coverage and precision are acceptable, GS, because of its incomplete recall, should not be used as a single source in SR searching. A specialized, curated medical database such as PubMed provides experienced searchers with tools and functionality that help improve recall, and numerous options in order to optimize precision. Searches for SRs should be performed by experienced searchers creating searches that maximize recall for as many databases as deemed necessary by the search expert.
机译:背景技术Google学术搜索(GS)作为用于生物医学系统评价(SR)搜索的书目数据库的有用性是当前研究界关注和争论的主题。最近的研究表明,GS甚至可以单独用于SR搜索中。通过测试GS是否可以找到21个先前发布的SR中包含的所有研究,在这里挑战了这一主张。其次,它考虑了可查看的最大项目数,检查了GS的召回,并测试了与21个已发布的SR中使用的搜索相比,信息专家创建的更完整的搜索是否会提高召回率。方法作者鉴定了21个以GS和PubMed为信息源的生物医学SR,并报告了它们在两个数据库中使用相同的可重复搜索策略。这些搜索策略已在GS和PubMed中重新运行,并对其覆盖率和召回率进行了分析。努力改善在每个数据库中表现不佳的搜索。结果GS的总体覆盖率高于PubMed(分别为98%和91%),GS的总体召回率更高:21个SR中包含的参考文献中有80%是通过GS的原始搜索返回的,而在PubMed中是68%。包含的参考文献中只有72%可以使用,因为它们被列在前1,000个匹配中(显示的最大数量)。实际精度(在前1000个中检索到的包含引用的数量除以1000)平均为1.9%,仅略低于其他已发布的SR。以较低的召回率来改善搜索,可以使GS的召回率从48%增加到66%,在PubMed中从60%增加到85%。结论尽管GS的覆盖范围和精度是可以接受的,但由于其不完全的召回性,不应将其用作SR搜索的单一来源。诸如PubMed之类的专业化精选医学数据库为经验丰富的搜索者提供了有助于改善召回率的工具和功能,并提供了多种选择来优化精度。 SR的搜索应由经验丰富的搜索者执行,以创建使搜索专家认为必要的数据库尽可能多地召回的搜索。

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