Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines.

ABSTRACT: Complete and transparent reporting of randomized controlled trial publications (RCTs) is essential for assessing their credibility. We aimed to develop text classification models for determining whether RCT publications report CONSORT checklist items. Using a corpus annotated with 37 fine-grained CONSORT items, we trained sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. We assessed the impact of data augmentation methods (Easy Data Augmentation (EDA), UMLS-EDA, text generation and rephrasing with GPT-4) on model performance. We also fine-tuned section-specific PubMedBERT models (e.g., Methods) to evaluate whether they could improve performance compared to the single full model. We performed 5-fol

SUBMITTER: Jiang L 

PROVIDER: S-EPMC11408668 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature