Obstacles to the reuse of study metadata in ClinicalTrials.gov

Abstract

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.

Evaluating FAIRness of Genomic Databases

“…For instance, the guarantee of persistence is a problematic point for all FAIR principles; its use adds greater control and reusability over (meta)data. Interestingly, Genbank had lower results on these modules, which substantiates previous findings in the literature [5,12].…”

“…However, they represent a small part of a group of more than 1,364 databases linked to the life sciences area [10]. Many of these archives are highly consolidated and have a long history, but it is also highly recognized in the academy that these same databases present issues of the most varied types [5,[11][12][13].…”

“…To structure the criteria, we scrutinized previous genomics analyses databases works [5,11,12], and also the FAIRsharing 6 and re3data 7 portals, in order to extract what is a trustworthy genomic database according to RaCE and MaCE. We developed the three levels of criteria that focus on how close a genomic database is to FAIR principles and linked to the metric score already described.…”

A Web-based Tool for Automatically linking Clinical Trials to their Publications

“…Third, previously proposed methods of linking trials to publications (e.g., overall matching of textual similarity [18] or shared authors between trials and publications [14]) have limited predictive performance on their own. Fourth, the textual fields and metadata of trial registries are not well standardized, [17,23,24] which complicates the process of matching specific textual fields of trials to those of publications. Finally, ancillary publications may arise from a trial concerning a wide variety of issues, such as questionnaire development, GWAS studies carried out on trial subjects, reanalysis of data across multiple trials, and so on, which may not share word usage, topics, or investigators with the registered trial entry.…”

Automatic Detection of Metadata Errors in a Registry of Clinical Studies Using Shapes Constraint Language (SHACL) Graphs

“…Additionally, as the metadata that needs to be submitted is not strongly enforced, e.g. by the use of ontologies or MeSH terms, it can only be re-used to a limited extend [4]. Thereby, several works proposed methods for automatically analyzing the data of ClinicalTrials.gov for detecting unusual patterns due to policy changes [5] or errors in metadata [1][4].…”