Occupational exposure to diesel engine emissions and risk of lung cancer: evidence from two case–control studies in Montreal, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the risk of lung cancer among men associated with exposure to diesel engine emissions incurred in a wide range of occupations and industries. METHODOLOGY: 2 population-based lung cancer case-control studies were conducted in Montreal. Study I (1979-1986) comprised 857 cases and 533 population controls; study II (1996-2001) comprised 736 cases and 894 population controls. A detailed job history was obtained, from which we inferred lifetime occupational exposure to 294 agents, including diesel engine emissions. ORs were estimated for each study and in the pooled data set, adjusting for socio-demographic factors, smoking history and selected occupational carcinogens. While it proved impossible to retrospectively estimate absolute exposure concentrations, there were estimates and analyses by relative measures of cumulative exposure. RESULTS: Increased risks of lung cancer were found in both studies. The pooled analysis showed an OR of lung cancer associated with substantial exposure to diesel exhaust of 1.80 (95% CI 1.3 to 2.6). The risk associated with substantial exposure was higher for squamous cell carcinomas (OR 2.09; 95% CI 1.3 to 3.2) than other histological types. Joint effects between diesel exhaust exposure and tobacco smoking are compatible with a multiplicative synergistic effect. DISCUSSION: Our findings provide further evidence supporting a causal link between diesel engine emissions and risk of lung cancer. The risk is stronger for the development of squamous cell carcinomas than for small cell tumours or adenocarcinomas.
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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) | 0.001 | 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".