在ClinicalTrials .gov的横断面分析中，使用文件相似性度量确定了试验注册和已发表文章之间未报告的联系
Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov
摘要：
目的：试验注册可用于衡量报告偏倚并支持系统评价，但45%的注册未提供与报告试验的文章的链接。我们评估了使用文档相似性方法来识别ClinicalTrials.gov和PubMed之间未报告的链接。研究设计和设置：我们从72，469个ClinicalTrials.gov注册和276，307篇PubMed文章的数据集中提取术语和概念，并测试了16，005个报告链接和90个手动识别的未报告链接的文章排名方法。性能通过匹配文章的中位数排名和未报告链接的比例来衡量，这些链接可以通过筛选按顺序排列的候选文章来发现。结果如下：表现最好的基于概念的表示产生了3（IQR 1-21）的报告链接和3（IQR 1-19）的手动识别的未报告链接的中位数排名，和基于术语的表示产生了2（1-20）的报告链接和2（IQR 1-12）的未报告链接的中位数排名。匹配的文章在40%的注册中排名第一，每次注册筛选50篇候选文章，确定了86%的未报告链接。结论：利用ClinicalTrials.gov和PubMed之间报告链接的语料库的增长，我们发现文档相似性方法可以帮助识别试验注册和相应文章之间未报告的链接。
英文摘要：
Objectives: Trial registries can be used to measure reporting biases and support systematic reviews but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. Study Design and Setting: We extracted terms and concepts from a dataset of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles, and tested methods for ranking articles across 16,005 reported links and 90 manually-identified unreported links. Performance was measured by the median rank of matching articles, and the proportion of unreported links that could be found by screening ranked candidate articles in order. Results: The best performing concept-based representation produced a median rank of 3 (IQR 1-21) for reported links and 3 (IQR 1-19) for the manually-identified unreported links, and term-based representations produced a median rank of 2 (1-20) for reported links and 2 (IQR 1-12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. Conclusions: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.
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