Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, 1265 Welch Rd, Stanford, CA, 94305, USA.
Sci Data. 2020 Dec 18;7(1):443. doi: 10.1038/s41597-020-00780-z.
Metadata that are structured using principled schemas and that use terms from ontologies are essential to making biomedical data findable and reusable for downstream analyses. The largest source of metadata that describes the experimental protocol, funding, and scientific leadership of clinical studies is ClinicalTrials.gov. We evaluated whether values in 302,091 trial records adhere to expected data types and use terms from biomedical ontologies, whether records contain fields required by government regulations, and whether structured elements could replace free-text elements. Contact information, outcome measures, and study design are frequently missing or underspecified. Important fields for search, such as condition and intervention, are not restricted to ontologies, and almost half of the conditions are not denoted by MeSH terms, as recommended. Eligibility criteria are stored as semi-structured free text. Enforcing the presence of all required elements, requiring values for certain fields to be drawn from ontologies, and creating a structured eligibility criteria element would improve the reusability of data from ClinicalTrials.gov in systematic reviews, metanalyses, and matching of eligible patients to trials.
使用原则性模式构建的元数据,以及使用本体论术语的元数据,对于使生物医学数据可查找和可重复用于下游分析至关重要。描述临床试验的实验方案、资金和科学领导的最大元数据来源是 ClinicalTrials.gov。我们评估了 302091 个试验记录中的值是否符合预期的数据类型并使用生物医学本体论中的术语,记录是否包含法规要求的字段,以及结构化元素是否可以替代自由文本元素。联系方式、结果衡量标准和研究设计经常缺失或未明确规定。搜索的重要字段,如条件和干预措施,不受限于本体论,并且几乎一半的条件未按建议用 MeSH 术语表示。入选标准存储为半结构化的自由文本。强制存在所有必需的元素,要求某些字段的值来自本体论,并创建结构化的入选标准元素,将提高从 ClinicalTrials.gov 系统评价、荟萃分析和合格患者与试验匹配中数据的可重用性。