Associations between occupational exposure to benzene, toluene and xylene and risk of lung cancer in Montréal
Bibliographic record
Abstract
BACKGROUND: Benzene, toluene and xylene (BTX) are aromatic hydrocarbons with inconclusive evidence of lung carcinogenicity. The aim of this research was to assess the associations between occupational exposures to BTX agents and lung cancer. METHODS: In a population-based case-control study of lung cancer, occupational histories were obtained and exposures were assessed by experts. Unconditional multivariate logistic regression was used to estimate ORs and 95% CIs, among men, between various metrics of occupational exposure to BTX and lung cancer, while adjusting for established and possible risk factors. RESULTS: Considerable overlap was found between occupational exposure to BTX, where the majority of exposed participants were exposed to all three chemicals. Lung cancer was associated with exposure to benzene (OR=1.35; 95% CI 0.99 to 1.84), toluene (OR=1.31; 95% CI 0.99 to 1.74) and xylene (OR=1.44; 95% CI 1.03 to 2.01). While these results were adjusted for smoking and other recognised and possible lung cancer risk factors, they were not mutually adjusted among the three BTX agents. CONCLUSIONS: Our study provides suggestive evidence that occupational exposure to one or more of the BTX agents may be associated with lung cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".