开发一种高效的搜索筛选器，从 PubMed 中检索系统评价。
Development of an efficient search filter to retrieve systematic reviews from PubMed.
出版信息
J Med Libr Assoc. 2021 Oct 1;109(4):561-574. doi: 10.5195/jmla.2021.1223.
Abstract
OBJECTIVE
Locating systematic reviews is essential for clinicians and researchers when creating or updating reviews and for decision-making in health care. This study aimed to develop a search filter for retrieving systematic reviews that improves upon the performance of the PubMed systematic review search filter.
METHODS
Search terms were identified from abstracts of reviews published in and the titles of articles indexed as systematic reviews in PubMed. Both the precision of the candidate terms and the number of systematic reviews retrieved from PubMed were evaluated after excluding the subset of articles retrieved by the PubMed systematic review filter. Terms that achieved a precision greater than 70% and relevant publication types indexed with MeSH terms were included in the filter search strategy.
RESULTS
The search strategy used in our filter added specific terms not included in PubMed's systematic review filter and achieved a 61.3% increase in the number of retrieved articles that are potential systematic reviews. Moreover, it achieved an average precision that is likely greater than 80%.
CONCLUSIONS
The developed search filter will enable users to identify more systematic reviews from PubMed than the PubMed systematic review filter with high precision.
摘要
目的
当临床医生和研究人员创建或更新综述以及进行医疗保健决策时，定位系统综述至关重要。本研究旨在开发一种检索系统综述的搜索过滤器，以提高 PubMed 系统综述搜索过滤器的性能。
方法
从发表的综述摘要和 PubMed 中索引为系统综述的文章标题中确定检索词。在排除 PubMed 系统综述过滤器检索到的文章子集后，评估候选术语的精度和从 PubMed 检索到的系统综述数量。包括精度大于 70%且使用 MeSH 术语索引的相关出版物类型的术语被纳入过滤器搜索策略。
结果
我们的过滤器中使用的搜索策略添加了 PubMed 系统综述过滤器中未包含的特定术语，并将检索到的可能是系统综述的文章数量增加了 61.3%。此外，它实现了平均精度可能大于 80%。
结论
开发的搜索过滤器将使用户能够从 PubMed 中比 PubMed 系统综述过滤器更精确地识别更多的系统综述。
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