0238 Lung cancer and exposure to benzene, toluene and xylene: results from two case-control studies in Montreal
Bibliographic record
Abstract
<h3>Objectives</h3> We aimed to evaluate the risks of lung cancer associated with exposure to benzene, to toluene and to xylene. <h3>Method</h3> Two population-based case-control studies conducted in Montreal included 1896 lung cancer cases and 1908 controls. Study I was conducted in 1980–1986, and study II in 1995–2001. Occupational exposures were assessed using a combination of subject-reported job history and expert assessment. Participants provided information on sociodemographic characteristics and smoking history. Using logistic regression, we evaluated the risk of lung cancer due to the exposure to each agent. <h3>Results</h3> Lifetime exposure prevalence ranged from 12% for xylene to 20% for benzene in study I, and 11% for xylene to 15% for benzene in study II. In both studies, 25% of the participants were exposed to benzene, toluene or xylene. Pooling studies, the odds ratios and 95% confidence interval (OR) for ever-exposure to any of the evaluated agents was 1.2 (1.0–1.4). In analyses including all subjects but only one agent at a time in the models, ORs were around 1.2–1.3 for each agent. When we excluded subjects ever exposed to two or three of these agents, none of the agents showed excess risk. Being ever exposed to all three agents was associated with lung cancer (OR: 1.3; 1.0–1.6). Attempts to estimate ORs for each agent while controlling for the two others resulted in co-linearity. <h3>Conclusions</h3> We found no clear indications of an association between lung cancer and exposure to toluene or xylene, but there was some evidence for an association with benzene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".