Identifying unreported links between ClinicalTrials.gov trial registrations and their published results.

ABSTRACT: A substantial proportion of trial registrations are not linked to corresponding published articles, limiting analyses and new tools. Our aim was to develop a method for finding articles reporting the results of trials that are registered on ClinicalTrials.gov when they do not include metadata links. We used a set of 27,280 trial registration and article pairs to train and evaluate methods for identifying missing links in both directions-from articles to registrations and from registrations to articles. We trained a classifier with six distance metrics as feature representations to rank the correct article or registration, using recall@K to evaluate performance and compare to baseline methods. When identifying links from registrations to published articles, the classifier ranked the correct

SUBMITTER: Liu S 

PROVIDER: S-EPMC9090946 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature