Dataset for the study: Evidence Triangulator: A Large Language Model Approach to Extracting and Synthesizing Causal Evidence across Study Designs

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 introduce Evidence Triangulator, a framework leveraging large language models to automate evidence triangulation through ontological and methodological extraction from scientific literature. A two-step extraction approach—focusing on exposure-outcome concepts first, followed by relation extraction—outperforms a one-step method, particularly in identifying the direction of effect (F1=0.86) and statistical significance (F1=0.96). Using salt consumption related health outcome as a case study, we calculate the Convergency of Evidence and Level of Convergency, finding a strong excitatory effect of salt on blood pressure (942 studies), and weak excitatory effect on cardiovascular diseases and mortality (124 studies). This approach complements traditional meta-analyses by integrating evidence across study designs, and enabling rapid, dynamic assessment of scientific controversies.