Lung cancer among coal miners, ore miners and quarrymen: smoking-adjusted risk estimates from the synergy pooled analysis of case–control studies
Bibliographic record
Abstract
OBJECTIVES: Working in mines and quarries has been associated with an elevated lung cancer risk but with inconsistent results for coal miners. This study aimed to estimate the smoking-adjusted lung cancer risk among coal miners and compare the risk pattern with lung cancer risks among ore miners and quarrymen. METHODS: We estimated lung cancer risks of coal and ore miners and quarrymen among 14 251 lung cancer cases and 17 267 controls from the SYNERGY pooled case-control study, controlling for smoking and employment in other at-risk occupations. RESULTS: Ever working as miner or quarryman (690 cases, 436 controls) was associated with an elevated odds ratio (OR) of 1.55 [95% confidence interval (95% CI) 1.34-1.79] for lung cancer. Ore miners (53 cases, 24 controls) had a higher OR (2.34, 95% CI 1.36-4.03) than quarrymen (67 cases, 39 controls; OR 1.92, 95% CI 1.21-3.05) and coal miners (442 cases, 297 controls; OR 1.40, 95% CI 1.18-1.67), but CI overlapped. We did not observe trends by duration of exposure or time since last exposure. CONCLUSIONS: This pooled analysis of population-based studies demonstrated an excess lung cancer risk among miners and quarrymen that remained increased after adjustment for detailed smoking history and working in other at-risk occupations. The increase in risk among coal miners were less pronounced than for ore miners or quarrymen.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".