Analysis of Mortality among Canadian Nuclear Power Industry Workers after Chronic Low-Dose Exposure to Ionizing Radiation
Bibliographic record
Abstract
Studies of radiation-associated risks among workers chronically exposed to low doses of radiation are important, both to estimate risks directly and to assess the adequacy of extrapolations of risk estimates from high-dose studies. This paper presents results based on a cohort of 45,468 nuclear power industry workers from the Canadian National Dose Registry monitored for more than 1 year for chronic low-dose whole-body ionizing radiation exposures sometime between 1957 and 1994 (mean duration of monitoring = 7.4 years, mean cumulative equivalent dose = 13.5 mSv). The excess relative risks for leukemia [excluding chronic lymphocytic leukemia (CLL)] and for all solid cancers were 52.5 [95% confidence interval (CI): 0.205, 291] and 2.80 (95% CI: -0.038, 7.13) per sievert, respectively, both associations having P values close to 0.05. Relative risks by dose categories increased monotonically for leukemia excluding CLL but were less consistent for all solid cancers combined. Although the point estimates are higher than those found in other studies of whole-body irradiation, the difference could well be due to chance. Further follow-up of this cohort or the combination of results from multiple worker studies will produce more stable estimates and thus complement the risk estimates from higher-dose studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".