Abstract B159: Characterizing interactions between genomic ancestry and social determinants of health and their implications for patient outcomes by leveraging LLM-annotated smoking data in a large clinicogenomic cohort

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This observational analysis explores genomic ancestry and social determinants of health, indicating distinct smoking behaviors among populations.

Patients with African and East Asian ancestry show different smoking phenotypes compared to Europeans, revealing ethnic disparities in smoking data.
Using large language models improved accuracy in annotating smoking behavior, particularly with GPT4o achieving 85% accuracy in pack year extraction.
Multivariate models indicate a strong association of smoking exposure with tumor mutational burden across various cancer types.
This study highlights the need for further research into how differences in smoking behavior affect patient outcomes within diverse ancestry groups.

Agbamu et al. (2025) studied this question.

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View Full Paper

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Authors

This observational analysis explores genomic ancestry and social determinants of health, indicating distinct smoking behaviors among populations.

Patients with African and East Asian ancestry show different smoking phenotypes compared to Europeans, revealing ethnic disparities in smoking data.
Using large language models improved accuracy in annotating smoking behavior, particularly with GPT4o achieving 85% accuracy in pack year extraction.
Multivariate models indicate a strong association of smoking exposure with tumor mutational burden across various cancer types.
This study highlights the need for further research into how differences in smoking behavior affect patient outcomes within diverse ancestry groups.

Agbamu et al. (2025) studied this question.

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