Occupational exposures to leaded and unleaded gasoline engine emissions and lung cancer risk
Bibliographic record
Abstract
OBJECTIVES: To determine whether occupational exposure to gasoline engine emissions (GEE) increased the risk of lung cancer and more specifically whether leaded or unleaded GEE increased the risk. METHODS: Two population-based case-control studies were conducted in Montreal, Canada. The first was conducted in the early 1980s and included many types of cancer including lung cancer. The second was conducted in the late 1990s and focused on lung cancer. Population controls were used in both studies. Altogether, there were 1595 cases and 1432 population controls. A comprehensive expert-based exposure assessment procedure was implemented and exposure was assessed for 294 agents, including unleaded GEE, leaded GEE and diesel engine emissions (DEE). Logistic regression analyses were conducted to estimate ORs between various metrics of GEE exposure and lung cancer, adjusting for smoking, DEE and other potential confounders. RESULTS: About half of all controls were occupationally exposed to GEE. Irrespective of the metrics of exposure (any exposure, duration of exposure and cumulative exposure) and the type of lung cancer, and the covariates included in models, none of the point estimates of the ORs between occupational exposure to leaded or unleaded GEE and lung cancer were above 1.0. Pooling two studies, the OR for any exposure to leaded GEE was 0.82 (0.68-1.00). CONCLUSIONS: Our results do not support the hypothesis that occupational exposure to GEE increases the risk of 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".