A large language model-based tool for identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame.

ABSTRACT:

Background

Industry-funded research poses a threat to the validity of scientific inference on carcinogenic hazards. Scientists require tools to better identify and characterize industry sponsored research across bodies of evidence to reduce the possible influence of industry bias in evidence synthesis reviews. We applied a novel large language model (LLM)-based tool named InfluenceMapper to demonstrate and evaluate its performance in identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame.

Methods

All epidemiological, animal cancer, and mechanistic studies included in systematic reviews on the carcinogenicity of the three agents by the IARC Monographs programme. Selected agents were recently evaluated by the Monographs and a

SUBMITTER: DeBono NL 

PROVIDER: S-EPMC12462328 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature