StudyTypeTeller-Large language models to automatically classify research study types for systematic reviews.

ABSTRACT: screening, a labor-intensive aspect of systematic review, is increasingly challenging due to the rising volume of scientific publications. Recent advances suggest that generative large language models like generative pre-trained transformer (GPT) could aid this process by classifying references into study types such as randomized-controlled trials (RCTs) or animal studies prior to abstract screening. However, it is unknown how these GPT models perform in classifying such scientific study types in the biomedical field. Additionally, their performance has not been directly compared with earlier transformer-based models like bidirectional encoder representations from transformers (BERT). To address this, we developed a human-annotated corpus of 2,645 PubMed titles and abstracts, annotated for

SUBMITTER: Emilova Doneva S 

PROVIDER: S-EPMC12657658 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature