Distribution of trial registry numbers within full-text of PubMed Central articles: implications for linking trials to publications and indexing trial publication types.

ABSTRACT:

Background

Linking registered clinical trials with their published results continues to be a challenge. A variety of natural language processing (NLP)-based and machine learning-based models have been developed to assist users in identifying these connections. To date, however, no system has attempted to detect mentions of registry numbers within the full-text of articles.

Methods

Articles from the PubMed Central full-text Open Access dataset were scanned for mentions of ClinicalTrials.gov and international clinical trial registry identifiers. We analyzed the distribution of trial registry numbers within sections of the articles and characterized their publication type indexing and other metrics.

Results

Registry numbers mentioned in article metadata (e.g., the abstra

SUBMITTER: Holt AM 

PROVIDER: S-EPMC11783733 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature