Cancer and mortality in coal mine workers: a systematic review and meta-analysis
Bibliographic record
Abstract
Coal mine workers are exposed to a number of workplace hazards which may increase the risk of cancer and mortality. We conducted a systematic review and meta-analysis to investigate cancer and mortality in coal mine workers We searched in Ovid Medline, PubMed, Embase and Web of Science databases using keywords and text words related to coal mines, cancer and mortality and identified 36 full-text articles using predefined inclusion criteria. Each study's quality was assessed using the Newcastle-Ottawa Scale. We performed random-effect meta-analyses including 21 of the identified articles evaluating cancer and/or mortality of coal mine workers. The meta-analysis showed an increased risk of all-cause mortality (SMR 1.14, 95% CI 1.00 to 1.30) and mortality from non-malignant respiratory disease (NMRD) (3.59, 95% CI 3.00 to 4.30) in cohorts with coal workers' pneumoconiosis (CWP). We found a somewhat increased risk of stomach cancer (1.11, 95% CI 0.97 to 1.35) and of mortality from NMRD (1.26, 95% CI 0.99 to 1.61) in the cohorts of coal miners with unknown CWP status. The meta-analysis also showed a decreased risk of prostate cancer and cardiovascular and cerebrovascular mortality among coal miners. This may be a result of the healthy worker effect and possible lower smoking rates, and perhaps also reflect the physically active nature of many jobs in coal mines. The meta-analysis for lung cancer did not show increased risk in coal miners with CWP (1.49, 95% CI 0.70 to 3.18) or for coal miners of unknown CWP status (1.03, 95% CI 0.91 to 1.18). Lower smoking rates in coal mine workers could explain why case-control studies where smoking was controlled for showed higher risks for lung cancer than were seen in cohort studies. Coal mine workers are at increased risk of mortality from NMRD but decreased risk of prostate cancer and cardiovascular and cerebrovascular mortality. Studies of coal mine workers need long-term follow-up to identify increased mortality and cancer incidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".