Cohort profile: four early uranium processing facilities in the US and Canada
Bibliographic record
Abstract
PURPOSE: Pooling of individual-level data for workers involved in uranium refining and processing (excluding enrichment) may provide valuable insights into risks from occupational uranium and external ionizing radiation exposures. METHODS: Data were pooled for workers from four uranium processing facilities (Fernald, Mallinckrodt and Middlesex from the U.S.; and Port Hope, Canada). Employment began as early as the 1930s and follow-up was as late as 2017. Workers were exposed to high concentrations of uranium, radium, and their decay products, as well as gamma radiation and ambient radon decay products. Exposure and outcome data were harmonized using similar definitions and dose reconstruction methods. Standardized mortality ratios (SMR) were estimated. RESULTS: In total, 560 deaths from lung cancer, 503 non-malignant respiratory diseases, 67 renal diseases, 1,596 ischemic heart diseases, and 101 dementia and Alzheimer's disease (AD) were detected in the pooled cohort of over 12,400 workers (∼1,300 females). Mean cumulative doses were 45 millisievert for whole-body external ionizing radiation exposure and 172 milligray for lung dose from radon decay products. Only SMR for dementia and AD among males was statistically significant (SMR=1.29; 95% confidence interval: 1.04, 1.54). CONCLUSIONS: This is the largest study to date to examine long-term health risks of uranium processing 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".