Academic Journal
Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure.
| Τίτλος: | Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure. |
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| Συγγραφείς: | Lo Cascio JN; From the NYU Grossman Long Island School of Medicine (GLISOM), Mineola, New York (J.N.L.C., N.M.); NYU Grossman School of Medicine (GSOM), New York, New York (C.J.O., R.B., F.H.); Department of Medicine, Division of Occupational Medicine, NYU GLISOM, Mineola, New York (M.W.); and Department of Medicine, NYU GLISOM, Mineola, New York (J.G.S., M.R.)., Mourikis N, Okpara CJ, Belenkaya R, Hussein F, Wilkenfeld M, Schneider JG, Rybstein M |
| Πηγή: | Journal of occupational and environmental medicine [J Occup Environ Med] 2026 Oct 01; Vol. 68 (10), pp. 773-779. Date of Electronic Publication: 2026 May 22. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9504688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-5948 (Electronic) Linking ISSN: 10762752 NLM ISO Abbreviation: J Occup Environ Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: Hagerstown, MD : Lippincott Williams & Wilkins Original Publication: Baltimore, MD : Williams & Wilkins, c1995- |
| Ιατρικοί όροι (MeSH): | Lung Neoplasms*/genetics , Lung Neoplasms*/epidemiology , Lung Neoplasms*/mortality , Lung Neoplasms*/etiology , September 11 Terrorist Attacks* , Large Language Models*, Smoking/epidemiology ; New York City/epidemiology ; Humans ; Female ; Male ; Middle Aged ; Disease Progression ; Registries ; Aged ; Mutation ; Adult |
| Περίληψη: | Objective: Leveraging large language models, we identified an unregistered subpopulation of individuals with World Trade Center-related exposure and lung cancer who were not previously captured in registries, and assessed how this exposure impacted patients' disease course. Methods: Associations between exposure type and smoking history, cancer stage, mutation status, disease progression, and survival were statistically analyzed. Results: The highest proportion of never-smokers was observed among residents, compared with First Responders and Commuters (19% and 24%; P = 0.005). Residents had more than twice the risk of disease progression (Hazard Ratio (HR) = 2.14, P = 0.008) and an elevated risk of death (HR = 2.43, P = 0.03). Only EGFR mutations were significantly associated with exposure type ( P = 0.01). Conclusions: This work highlights that large language model can capture a greater population of World Trade Center survivors, including genetics. (Copyright © 2026 American College of Occupational and Environmental Medicine.) |
| Competing Interests: | Conflict of Interest: The authors have no conflicts of interest to disclose. |
| Contributed Indexing: | Keywords: 9/11; World Trade Center; large language models; primary lung cancer |
| Entry Date(s): | Date Created: 20260522 Date Completed: 20260925 Latest Revision: 20260925 |
| Update Code: | 20260925 |
| DOI: | 10.1097/JOM.0000000000003760 |
| PMID: | 42168810 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1536-5948 |
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| DOI: | 10.1097/JOM.0000000000003760 |