Diesel Engine Exhaust Exposure, Smoking, and Lung Cancer Subtype Risks. A Pooled Exposure–Response Analysis of 14 Case–Control Studies
Bibliographic record
Abstract
Abstract Rationale Although the carcinogenicity of diesel engine exhaust has been demonstrated in multiple studies, little is known regarding exposure–response relationships associated with different exposure subgroups and different lung cancer subtypes. Objectives We expanded on a previous pooled case–control analysis on diesel engine exhaust and lung cancer by including three additional studies and quantitative exposure assessment to evaluate lung cancer and subtype risks associated with occupational exposure to diesel exhaust characterized by elemental carbon (EC) concentrations. Methods We used a quantitative EC job-exposure matrix for exposure assessment. Unconditional logistic regression models were used to calculate lung cancer odds ratios and 95% confidence intervals (CIs) associated with various metrics of EC exposure. Lung cancer excess lifetime risks (ELR) were calculated using life tables accounting for all-cause mortality. Additional stratified analyses by smoking history and lung cancer subtypes were performed in men. Measurements and Main Results Our study included 16,901 lung cancer cases and 20,965 control subjects. In men, exposure response between EC and lung cancer was observed: odds ratios ranged from 1.09 (95% CI, 1.00–1.18) to 1.41 (95% CI, 1.30–1.52) for the lowest and highest cumulative exposure groups, respectively. EC-exposed men had elevated risks in all lung cancer subtypes investigated; associations were strongest for squamous and small cell carcinomas and weaker for adenocarcinoma. EC lung cancer exposure response was observed in men regardless of smoking history, including in never-smokers. ELR associated with 45 years of EC exposure at 50, 20, and 1 μg/m3 were 3.0%, 0.99%, and 0.04%, respectively, for both sexes combined. Conclusions We observed a consistent exposure–response relationship between EC exposure and lung cancer in men. Reduction of workplace EC levels to background environmental levels will further reduce lung cancer ELR in exposed workers.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".