Data for: A Large Language Model Approach to Extracting Causal Evidence across Study Designs for Evidence Triangulation

Health strategies increasingly emphasize both behavioral and biomedical interventions, yet the complex and often contradictory guidance on diet, behavior, and health outcomes complicates evidence-based decision-making. Evidence triangulation across diverse study designs is essential for balancing biases and establishing causality, but scalable, automated methods for achieving this are lacking. In this study, we assess the performance of large language models (LLMs) in extracting both ontological and methodological information from scientific literature to automate evidence triangulation. A two-step extraction approach—focusing on factor-effect concepts first, followed by relation extraction—outperformed a one-step method, particularly in identifying effect direction and statistical significance. Using salt intake and blood pressure as a case study, we calculated the Convergeny of Evidence (CoE) and Level of Convergency (LoC), finding a strong excitatory effect of salt on blood pressure, and weak excitatory effect on cardiovascular diseases and deaths. This approach complements traditional meta-analyses by integrating evidence across study designs, also providing dynamic tracking of evidence on controversies.