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[Preprint]. 2025 Jun 10:2025.06.09.25329285.
doi: 10.1101/2025.06.09.25329285.

Linking Trials to Publications: Enhancing Recall by Identifying Trial Registry Mentions in Full-Text

Arthur W Holt  1 , Neil R Smalheiser  1
Affiliations

Linking Trials to Publications: Enhancing Recall by Identifying Trial Registry Mentions in Full-Text

Arthur W Holt et al. medRxiv. .

Abstract

We have developed a free, public web-based tool, Trials to Publications, https://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/TrialPubLinking/trial_pub_link_start.cgi, which employs a machine learning model to predict which publications are likely to present clinical outcome results from a given registered trial in ClinicalTrials.gov. The tool has reasonably high precision, yet in a recent study we found that when registry mentions are not explicitly listed in metadata, textual clues (in title, abstract or other metadata) could identify only roughly 1/3-1/2 of the publications with high confidence. This finding has led us to expand the scope of the tool, to search for explicit mentions of registry numbers that are located within the full-text of publications. We have now retrieved ClinicalTrials.gov registry number mentions (NCT numbers) from the full-text of 3 online biomedical article collections (open access PubMed Central, EuroPMC, and OpenAlex), as well as retrieving biomedical citations that are mentioned within the ClinicalTrials.gov registry itself. These methods greatly increase the recall of identifying linked publications, and should assist those carrying out evidence syntheses as well as those studying the meta-science of clinical trials.

Keywords: clinical trials; information retrieval; linking trials to publications; machine learning; systematic reviews.

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Figures

Figure 1.
Figure 1.
Cumulative % of Trials with at least One Registry Mention by Source
See this image and copyright information in PMC

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