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Can electronic search engines optimize screening of search results in systematic reviews: an empirical study

机译:电子搜索引擎能否优化系统评价中搜索结果的筛选:一项实证研究

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Background Most electronic search efforts directed at identifying primary studies for inclusion in systematic reviews rely on the optimal Boolean search features of search interfaces such as DIALOG? and Ovid?. Our objective is to test the ability of an Ultraseek? search engine to rank MEDLINE? records of the included studies of Cochrane reviews within the top half of all the records retrieved by the Boolean MEDLINE search used by the reviewers. Methods Collections were created using the MEDLINE bibliographic records of included and excluded studies listed in the review and all records retrieved by the MEDLINE search. Records were converted to individual HTML files. Collections of records were indexed and searched through a statistical search engine, Ultraseek, using review-specific search terms. Our data sources, systematic reviews published in the Cochrane library, were included if they reported using at least one phase of the Cochrane Highly Sensitive Search Strategy (HSSS), provided citations for both included and excluded studies and conducted a meta-analysis using a binary outcome measure. Reviews were selected if they yielded between 1000–6000 records when the MEDLINE search strategy was replicated. Results Nine Cochrane reviews were included. Included studies within the Cochrane reviews were found within the first 500 retrieved studies more often than would be expected by chance. Across all reviews, recall of included studies into the top 500 was 0.70. There was no statistically significant difference in ranking when comparing included studies with just the subset of excluded studies listed as excluded in the published review. Conclusion The relevance ranking provided by the search engine was better than expected by chance and shows promise for the preliminary evaluation of large results from Boolean searches. A statistical search engine does not appear to be able to make fine discriminations concerning the relevance of bibliographic records that have been pre-screened by systematic reviewers.
机译:背景技术大多数旨在确定要纳入系统评价的基础研究的电子搜索工作都依赖于诸如DIALOG ?之类的搜索界面的最佳布尔搜索功能。 和Ovid?我们的目标是测试Ultraseek 的能力? 搜索引擎对MEDLINE 进行排名?审查者使用的布尔MEDLINE搜索检索到的所有记录的上半部分中,Cochrane评论纳入研究的记录。方法使用评价中列出的纳入和排除研究的MEDLINE书目记录以及通过MEDLINE搜索检索的所有记录来创建馆藏。记录被转换为单个HTML文件。使用特定于审阅的搜索词,通过统计搜索引擎Ultraseek对记录的集合进行索引和搜索。如果我们使用至少一个阶段的Cochrane高敏感度搜索策略(HSSS)报告了我们的数据来源,即发表在Cochrane图书馆中的系统评价,则为包含和排除的研究提供了引文,并使用二元进行了荟萃分析结果测量。如果在复制MEDLINE搜索策略时产生1000-6000条记录,则选择评论。结果纳入9项Cochrane评价。 Cochrane评价中包括的研究在前500篇检索的研究中发现的机会比偶然预期的要多。在所有评论中,前500强的纳入研究的回忆率为0.70。将纳入的研究与已发表评论中列出的排除研究的子集进行比较时,排名没有统计学上的显着差异。结论搜索引擎提供的相关性排名好于偶然的预期,并显示了对布尔搜索的大型结果进行初步评估的希望。统计搜索引擎似乎无法对已由系统评审员预先筛选的书目记录的相关性进行精细区分。

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