Klein Sean A, Baiocchi Michael, Rodu Jordan, Baker Heather, Rosemond Erica, Doyle Jamie Mihoko
Office of Science and Data Policy, Office of the Assistant Secretary for Planning and Evaluation, US Department of Health and Human Services, Washington, DC, USA.
Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
J Clin Transl Sci. 2022 Aug 18;6(1):e113. doi: 10.1017/cts.2022.444. eCollection 2022.
Pilot projects ("pilots") are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators.
To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions.
The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs.
Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs.
试点项目（“试点”）对于在投入更多资金进行全面研究之前检验假设非常重要。对于一些项目，如由国家转化科学中心支持的临床与转化科学奖（CTSA），试点项目在直接由CTSA支持开展的研究项目中也占很大比例。不幸的是，关于试点项目的管理数据通常不会被收录在可访问的数据库中。虽然试点项目的数据包含在研究绩效进展报告中，但往往难以提取，特别是对于像CTSA这样的大型项目，每年所有获奖者可能会报告600多个试点项目。数据提取方面的挑战使得无法进行相关分析，而这些分析本可为研究人员和管理人员提供有关试点项目的有价值信息。
为应对这些挑战，我们描述了一个脚本，该脚本可部分自动从CTSA研究进展报告中提取试点项目数据。提取试点项目数据后，我们使用一个既定的机器学习（ML）模型来确定试点项目的科学内容，以便后续分析。对ML分配的科学类别进行分析，揭示了CTSA试点项目组合的科学多样性以及各个试点项目与机构之间的关系。
CTSA试点项目广泛分布在多个科学领域。内容分析确定了类似项目以及各中心之间科学兴趣的重叠程度。
我们的结果表明，试点项目数据的提取仍然具有挑战性，但可为与利益相关者沟通、管理试点项目组合以及促进研究人员和中心之间的合作提供有用信息。
