Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH, Bethesda, MD, USA.
BMC Med Res Methodol. 2022 Aug 10;22(1):221. doi: 10.1186/s12874-022-01703-9.
In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. Our objective was to develop a data science approach to identify and analyze all publications linked to COVID-19 clinical studies and generate a prioritized list of publications for efficient understanding of the state of COVID-19 clinical research.
We conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications.
The performed searchers resulted in 1 022 articles linked to 565 interventional trials (17.8% of all 3 167 COVID-19 interventional trials as of 31 January 2022). 609 publications were identified via abstract-link in PubMed and 413 via registry-link in ClinicalTrials.gov, with 27 articles linked from both sources. Of the 565 trials publishing at least one article, 197 (34.9%) had multiple linked publications. An attention score was assigned to each publication to develop a prioritized list of all publications linked to COVID-19 trials and 83 publications were identified that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, 108 linked result articles for 64 trials (14.7% of 436 total COVID-19 vaccine trials) were found.
Our method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link. Our data science methodology also allows for consistent and as needed data updates and is generalizable to other conditions of interest.
为应对 COVID-19 大流行，启动了许多临床研究，这需要有效方法来跟踪和分析研究结果。我们扩展了之前跟踪注册 COVID-19 临床研究的项目，以跟踪这些研究产生的结果文章。我们的目标是开发一种数据科学方法来识别和分析与 COVID-19 临床研究相关的所有出版物，并生成一份优先出版物清单，以便高效了解 COVID-19 临床研究的现状。
我们在 ClinicalTrials.gov 和 PubMed 上进行了搜索，以识别与 COVID-19 研究相关的文章，并根据试验阶段、干预措施、地点和记录的最新情况制定了标准，以制定一份优先出版物清单。
进行的搜索导致与 565 项干预性试验相关的 1 022 篇文章（截至 2022 年 1 月 31 日，所有 3 167 项 COVID-19 干预性试验的 17.8%）。通过 PubMed 的摘要链接识别了 609 篇出版物，通过 ClinicalTrials.gov 的注册链接识别了 413 篇，有 27 篇文章来自两个来源。在发表至少一篇文章的 565 项试验中，有 197 项（34.9%）有多个相关出版物。为每个出版物分配了一个关注度得分，以开发一份与 COVID-19 试验相关的所有出版物的优先清单，并确定了 83 篇来自至少有一个美国站点和多个研究记录更新的后期（第 3 阶段）试验的结果文章。对于 COVID-19 疫苗试验，发现了与 64 项试验相关的 108 篇结果文章（436 项 COVID-19 疫苗试验的 14.7%）。
我们的方法允许有效识别报告注册临床试验结果的重要 COVID-19 文章，并通过结构化的文章-试验链接进行连接。我们的数据科学方法还允许进行一致且按需的数据更新，并且可以推广到其他感兴趣的条件。
