Burden of lung cancer attributable to occupational diesel engine exhaust exposure in Canada
Bibliographic record
Abstract
OBJECTIVE: To estimate the population attributable fraction (PAF) and number of incident and fatal lung cancers in Canada from occupational exposure to diesel engine exhaust (DEE). METHODS: DEE exposure prevalence and level estimates were used with Canadian Census and Labour Force Survey data to model the exposed population across the risk exposure period (REP, 1961-2001). Relative risks of lung cancer were calculated based on a meta-regression selected from the literature. PAFs were calculated using Levin's equation and applied to the 2011 lung cancer statistics obtained from the Canadian Cancer Registry. RESULTS: We estimated that 2.4% (95% CI 1.6% to 6.6%) of lung cancers in Canada are attributable to occupational DEE exposure, corresponding to approximately 560 (95% CI 380 to 1570) incident and 460 (95% CI 310 to 1270) fatal lung cancers in 2011. Overall, 1.6 million individuals alive in 2011 were occupationally exposed to DEE during the REP, 97% of whom were male. Occupations with the highest burden were underground miners, truck drivers and mechanics. Half of the attributable lung cancers occurred among workers with low exposure. CONCLUSIONS: This is the first study to quantify the burden of lung cancer attributable to occupational DEE exposure in Canada. Our results underscore a large potential for prevention, and a large public health impact from occupational exposure to low levels of DEE.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".