We have previously described a free, public web-based tool, Trials to Publications, https: //arrowsmith. psych. uic. edu/cgi-bin/arrowsmithᵤic/TrialPubLinking/trialₚubₗinkₛtart. cgi, which employs a machine-learning model based on title, abstract, and other metadata features to predict which publications are likely to present clinical outcome results from a given registered trial in ClinicalTrials. gov. We have now updated and expanded the scope of the tool, by extracting mentions of ClinicalTrials. gov registry numbers (NCT numbers) from the full-text of 3 online biomedical article collections (open access PubMed Central (PMC), EuroPMC, and OpenAlex), as well as retrieving biomedical publications that are mentioned within the ClinicalTrials. gov registry itself. These mentions greatly increase the number of linked publications identified by the tool and should assist those carrying out evidence syntheses as well as those studying the metascience of clinical trials.
Holt et al. (2026) studied this question.