Mortality among uranium miners in North America and Europe: the Pooled Uranium Miners Analysis (PUMA)
Bibliographic record
Abstract
BACKGROUND: The Pooled Uranium Miners Analysis (PUMA) study draws together information from cohorts of uranium miners from Canada, the Czech Republic, France, Germany and the USA. METHODS: Vital status and cause of death were ascertained and compared with expectations based upon national mortality rates by computing standardized mortality ratios (SMRs) overall and by categories of time since first hire, calendar period of first employment and duration of employment as a miner. RESULTS: There were 51 787 deaths observed among 118 329 male miners [SMR = 1.05; 95% confidence interval (CI): 1.04, 1.06]. The SMR was elevated for all cancers (n = 16 633, SMR = 1.23; 95% CI: 1.21, 1.25), due primarily to excess mortality from cancers of the lung (n = 7756, SMR = 1.90; 95% CI: 1.86, 1.94), liver and gallbladder (n = 549, SMR = 1.15; 95% CI: 1.06, 1.25), larynx (n = 229, SMR = 1.10; 95% CI: 0.97, 1.26), stomach (n = 1058, SMR = 1.08; 95% CI: 1.02, 1.15) and pleura (n = 39, SMR = 1.06; 95% CI: 0.75, 1.44). Lung-cancer SMRs increased with duration of employment, decreased with calendar period and persisted with time since first hire. Among non-malignant causes, the SMR was elevated for external causes (n = 3362, SMR = 1.41; 95% CI: 1.36, 1.46) and respiratory diseases (n = 4508, SMR = 1.32; 95% CI: 1.28, 1.36), most notably silicosis (n = 814, SMR = 13.56; 95% CI: 12.64, 14.52), but not chronic obstructive pulmonary disease (n = 1729, SMR = 0.98; 95% CI: 0.93, 1.02). CONCLUSIONS: Whereas there are important obstacles to the ability to detect adverse effects of occupational exposures via SMR analyses, PUMA provides evidence of excess mortality among uranium miners due to a range of categories of cause of death. The persistent elevation of SMRs with time since first hire as a uranium miner underscores the importance of long-term follow-up of these workers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".