Occupational exposure to polycyclic aromatic hydrocarbons and lung cancer risk: results from a pooled analysis of case-control studies (SYNERGY)
Bibliographic record
Abstract
Background: Exposure to polycyclic aromatic hydrocarbons (PAH) occurs widely in occupational settings. We investigated the association between occupational exposure to PAH and lung cancer risk and joint effects with smoking within the SYNERGY project. Methods: We pooled 14 case–control studies with information on lifetime occupational and smoking histories conducted between 1985 and 2010 in Europe and Canada. Exposure to benzo[a]pyrene (BaP) was used as a proxy of PAH and estimated from a quantitative general population job-exposure matrix. Multivariable unconditional logistic regression models, adjusted for smoking and exposure to other occupational lung carcinogens, estimated ORs, and 95% confidence intervals (CI). Results: We included 16,901 lung cancer cases and 20,965 frequency-matched controls. Adjusted OR for PAH exposure (ever) was 1.08 (CI, 1.02–1.15) in men and 1.20 (CI, 1.04–1.38) in women. When stratified by smoking status and histologic subtype, the OR for cumulative exposure ≥0.24 BaP mg/m3-years in men was higher in never smokers overall [1.31 (CI, 0.98–1.75)], for small cell [2.53 (CI, 1.28–4.99)] and squamous cell cancers [1.33 (CI, 0.80–2.21)]. Joint effects between PAH and smoking were observed. Restricting analysis to the most recent studies showed no increased risk. Conclusions: Elevated lung cancer risk associated with PAH exposure was observed in both sexes, particularly for small cell and squamous cell cancers, after accounting for cigarette smoking and exposure to other occupational lung carcinogens. Impact: The lack of association between PAH and lung cancer in more recent studies merits further research under today’s exposure conditions and worker protection measures.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".