Exposure to Diesel and Gasoline Engine Emissions and the Risk of Lung Cancer
Bibliographic record
Abstract
Pollution from motor vehicles constitutes a major environmental health problem. The present paper describes associations between diesel and gasoline engine emissions and lung cancer, as evidenced in a 1979-1985 population-based case-control study in Montreal, Canada. Cases were 857 male lung cancer patients. Controls were 533 population controls and 1,349 patients with other cancer types. Subjects were interviewed to obtain a detailed lifetime job history and relevant data on potential confounders. Industrial hygienists translated each job description into indices of exposure to several agents, including engine emissions. There was no evidence of excess risks of lung cancer with exposure to gasoline exhaust. For diesel engine emissions, results differed by control group. When cancer controls were considered, there was no excess risk. When population controls were studied, the odds ratios, after adjustments for potential confounders, were 1.2 (95% confidence interval: 0.8, 1.8) for any exposure and 1.6 (95% confidence interval: 0.9, 2.8) for substantial exposure. Confidence intervals between risk estimates derived from the two control groups overlapped considerably. These results provide some limited support for the hypothesis of an excess lung cancer risk due to diesel exhaust but no support for an increase in risk due to gasoline exhaust.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".