Developing Methods to Improve Systematic Reviews Using Clinical Trial Registries

项目摘要 系统评价是支持政策和临床决策的关键信息源，并且 预计将提供对临床已知情况的全面、最新和公正的评估 干预。传统的系统评审是资源密集型的工作，而且随着快速和 证据产生的速度越来越快，要确保系统审查变得越来越困难 是最新的。虽然解决这一挑战的创新以前专注于自动化 特定的任务，如筛选和数据提取，现在需要利用 新的数据源，并考虑跨多组审查的效率。该提案使用了一种独特的资源-- 易易工作室-各种在线工具，站长网志，以及多个应用项目。 它们在完成和报告时，并在系统审查需要更新时发出信号。我们建议 调查与针对肥胖和2型糖尿病的干预措施相关的系统评价语料库，以 (1)开发和评估基于图的半监督学习方法 用于识别来自ClinicalTrials.gov的相关临床试验并将其与系统评价联系起来；以及(2)创建 并用反映最新信息的数据填充动态更新的系统评价数据库 对新出现的审判证据的看法。我们将使本提案中开发的工具和数据库免费 可供系统审查社区使用。因此，我们的工作不仅将引入新的方法来 确定相关证据，但也将提供持续的资源，以支持系统审查员 确定系统评审更新的优先顺序，确保评审是对以下各项的全面和及时的总结 科学证据。

Project Summary Systematic reviews are a critical information source supporting policy and clinical decision-making, and are expected to provide a comprehensive, current, and unbiased assessment of what is known about a clinical intervention. Traditional systematic reviews are resource-intensive endeavors, and with the rapid and increasing pace of evidence production, it is becoming increasingly difficult to ensure that systematic reviews are kept up-to-date. While innovations addressing this challenge have previously focused on automating the specific tasks such as screening and data extraction, innovative approaches are now needed that leverage new data sources and consider efficiencies across sets of reviews. This proposal uses a unique resource— ClinicalTrials.gov—as a data source to develop tools that automatically identify relevant clinical trials, track them as they are completed and reported, and signal when a systematic review requires updating. We propose to investigate a corpus of systematic reviews related to interventions targeting obesity and type 2 diabetes to address the two following aims: (1) To develop and evaluate graph-based semi-supervised learning methods for identifying and linking relevant clinical trials from ClinicalTrials.gov to systematic reviews; and (2) To create and populate a dynamically-updated database of systematic reviews with data that reflects the most up-to-date view of emerging trial evidence. We will make the tools and database developed in this proposal freely available for use by the systematic review community. Thus, our work will not only introduce new ways to identify relevant evidence but will also provide an ongoing resource to support systematic reviewers in prioritizing systematic review updates and ensuring that reviews are a comprehensive and timely summary of the scientific evidence.