Cancer and mortality in coal mine workers: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Coal mine workers are exposed to workplace hazards such as silica and coal dust which may increase the risk of ill health. Aim: To conduct a systematic review and meta-analyses of cancer and mortality in coal mine workers. Methods: We searched Ovid Medline, PubMed, and Embase databases using words related to coal mines, cancer and mortality and identified full-text articles. We used the Newcastle-Ottawa Scale to assess study quality. We performed a random-effect meta-analysis, including 26 of the 36 identified studies evaluating cancer and/or mortality risks. The review is registered with PROSPERO, CRD42020199199. Results: Only studies of male coal mine workers were found. 64% of papers were scored as good quality. The meta-analysis showed an increased risk of all-cause and mortality from non-malignant respiratory disease (NMRD) in cohorts with coal workers’ pneumoconiosis (CWP). We found an increased risk of stomach cancer and of mortality from NMRD in the cohorts of coal miners with unknown CWP status. The meta-analyses showed a decreased risk of prostate cancer, cardiovascular and cerebrovascular diseases. This may be related to the Healthy Worker Effect, possible lower smoking rates and perhaps the physically active work. The meta-analysis for lung cancer suggested an increased risk in coal miners with CWP but not in miners of unknown CWP status. Case-control studies tended to show higher risks than cohort studies which may be smoking-related. Conclusion: Given the large number of workers exposed to coal mine dust and the long latent period of most diseases, further research with long-term follow-up and personalised smoking data is required.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".