Towards Automating Articles Screening Processes Using Chain-of-Thought Large Language Models

The rapid expansion of the biomedical literature has made manual screening for meta-analyses increasingly hard. While automation attempts exist, they often rely on active-learning powered binary classification, which is still time intensive; or simple summarization, frequently struggling with factual consistency and completeness of the information reported. This work presents a novel pipeline using Reasoning-Large Language Models (Chain-of-Thought) to automate the summarization of informative elements from full-text biomedical documents, focusing on PIO elements (Patient/Population, Intervention, and Outcome) in a human-in-the-loop scenario. PDF documents are segmented into structured XML semantic chunks. Then, the pipeline employs a Large Language Model, where several prompts are adaptively used based on the semantic complexity of the chunks, measured using Effective Rank. Consensus-based filtering mechanisms are then used to synthesize multiple candidate responses into a single, high-fidelity summary.