Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation

Our work demonstrates the potential of fine-tuned, domain-specific language models to accurately identify publications reporting on complex and specialized study designs, addressing a critical need in the public health research community. This model offers a valuable tool for the public health community to directly identify publications from clinical trials that use one of the three classes of nested designs.