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Record W4225359625 · doi:10.1097/jom.0000000000002481

Occupational Exposures and Lung Cancer Risk—An Analysis of the CARTaGENE Study

2022· article· en· W4225359625 on OpenAlexaffabout
Saeedeh Moayedi-Nia, Romain Pasquet, Jack Siemiatycki, Anita Koushik, Vikki Ho

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLung cancerEnvironmental healthMedicineOccupational exposureOccupational medicineOncology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the associations between prevalent occupational agents and lung cancer risk. METHODS: A case-cohort design (ncases= 147; nsub-cohort= 1,032) was nested within the CARTaGENE prospective cohort study. The Canadian Job Exposure Matrix was used to determine the probability of exposure to 27 agents in participants' longest-held jobs. Multivariable logistic regression with robust variance estimators was used to determine the associations between each agent and lung cancer risk while adjusting for established lung cancer risk factors. RESULTS: Increased lung cancer risk was observed among those exposed to ashes, calcium sulfate, formaldehyde, cooking fumes, alkanes, aliphatic aldehydes, and cleaning agents. Lower lung cancer risk was found among participants exposed to carbon monoxide and polycyclic aromatic hydrocarbons from petroleum. CONCLUSION: Our findings support the role of several occupational agents, for which we have limited knowledge, in contributing to lung cancer risk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.308
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2022
Admission routes2
Has abstractyes

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