Protracted exposure to low-dose ionising radiation and cancer incidence among Canadian nuclear power plant workers
Bibliographic record
Abstract
OBJECTIVES: Ionising radiation is a human carcinogen; however, there are uncertainties about the shape of the exposure-response function at low doses. We evaluated the relationship between radiation dose and cancer incidence in a cohort of Canadian nuclear power plant workers (NPPWs) with protracted exposures to low-dose ionising radiation. METHODS: The cohort included 75 350 workers employed at one of five Canadian nuclear power plants any time between 1945 and 2010. Exposure to cumulative whole-body effective dose was determined through personal monitoring. A total of 4370 incident cancers were identified through record linkage of these workers to national cancer registries (1969-2010). Vital status was determined through linkages to national mortality and tax databases. Standardised incidence ratios (SIRs) were calculated to compare cancer incidence rates of the cohort with the Canadian general population. Poisson regression was used to characterise dose-response relationships via categorical and linear excess relative risk (ERR) models. RESULTS: Significantly elevated SIRs were found for solid cancers (combined), melanoma, colon and prostate cancer, while a reduced SIR was found for lung cancer. Positive, but not statistically significant excess risks were found for melanoma (ERR/100 mSv=0.32; 95% CI: -0.23 to 0.87) and prostate cancer (ERR/100 mSv=0.12; 95% CI: -0.05 to 0.29). An inverse association was found for lung cancer (ERR/100 mSv=-0.18; 95% CI: -0.01 to -0.36). CONCLUSIONS: Our findings suggest that Canadian NPPWs have increased risks of prostate cancer and melanoma from low-dose ionising radiation exposure. Estimates should be cautiously interpreted due to the inability to adjust for demographic and lifestyle factors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".