Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs
Abstract Health strategies increasingly emphasize both behavioural 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 ba...
Saved in:
| Main Authors: | Xuanyu Shi, Wenjing Zhao, Ting Chen, Chao Yang, Jian Du |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62783-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Prenatal maternal stress: triangulating evidence for intrauterine exposure effects on birth and early childhood outcomes across multiple approaches
by: Ingunn Olea Lund, et al.
Published: (2025-01-01) -
Careful design of Large Language Model pipelines enables expert-level retrieval of evidence-based information from syntheses and databases.
by: Radhika Iyer, et al.
Published: (2025-01-01) -
Non‐genetic biomarkers and colorectal cancer risk: Umbrella review and evidence triangulation
by: Xiaomeng Zhang, et al.
Published: (2020-07-01) -
Large Language Models (LLMs) and Causality Extraction from Text
by: Wlodek Zadrozny
Published: (2025-05-01) -
The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
by: Jack Bowden, et al.
Published: (2021-09-01)