EvidenceTriangulator: A Large Language Model Approach to Synthesizing Causal Evidence across Study Designs

In managing chronic diseases, the role of social determinants like lifestyle and diet is crucial. A comprehensive strategy combining biomedical and lifestyle changes is necessary for optimal health. However, the complexity of evidence from varied study designs on lifestyle interventions poses a challenge to decision-making. To tackle this challenge, our work focused on leveraging large language model to construct a dataset primed for evidence triangulation. This approach automates the process of gathering and preparing evidence for analysis, thereby simplifying the integration of reliable insights and reducing the dependency on labor-intensive manual curation. Our approach, validated by expert evaluations, demonstrates significant utility, especially illustrated through a case study on reduced salt intake and its effect on blood pressure. This highlights the potential of leveraging large language models to enhance evidence-based decision-making in health care. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the National Key R&D Program for Young Scientists (Project number 2022YFF0712000 to JD) and the National Natural Science Foundation of China (Project number 72074006 to JD). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors