What question did this study set out to answer?

This research aims to identify lung cancer patients with unregistered World Trade Center exposure and understand how this exposure influences their disease.

May 24, 2026Journal of Occupational and Environmental Medicine

Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure

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Authors

JCJulia Nancy Lo CascioLong Island University NMNicholas MourikisLong Island University COChinyere J. OkparaNew York University

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Implication

Randomized trial assesses lung cancer progression and genetics in WTC-exposed individuals, suggesting improved identification methods.

Key Points

This research aims to identify lung cancer patients with unregistered World Trade Center exposure and understand how this exposure influences their disease.
Leveraged large language models to identify an unregistered subpopulation of WTC exposure individuals.
Statistical analysis of associations between exposure, smoking history, cancer stage, mutation status, and survival.
Analyzed data on a population of lung cancer patients with a focus on various exposure types.
Residents exhibited a higher prevalence of never-smokers compared to first responders and commuters (19% vs. 24%; p = 0.005).
Residents faced more than double the risk of disease progression (HR = 2.14, p = 0.008) and elevated risk of death (HR = 2.43, p = 0.03).
EGFR mutations were significantly associated with exposure type (p = 0.01).

Cite This Study

Cascio et al. (2026) studied this question.

synapsesocial.com/papers/6a12966a48a0ea16656732fe https://doi.org/10.1097/jom.0000000000003760

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Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure

Discussion

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Implication

Key Points

Cite This Study

Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure

Discussion

Member takes

Implication

Key Points

Cite This Study

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