O6B.3 Risk of leukemia after chronic exposure to gamma radiation among ontario uranium miners?
Bibliographic record
Abstract
Background and objectives Increases in leukemia risk after exposure to gamma radiation have been well-demonstrated among nuclear energy workers and atomic bomb survivors. Although uranium miners are also exposed to gamma radiation, its health effects are not well characterized, and assumed to be insignificant relative to the effects of radon decay products. The objective of this study is to quantify the effects of whole-body gamma radiation exposure on the incident risk of leukemia among Ontario Uranium Miners. Methods Based on a retrospective cohort of 28 546 uranium miners, leukemia cases were identified through record linkages with the Canadian Cancer Registry and Canadian Mortality Database. Gamma doses were estimated through dose prediction models and badge dosimeter readings collated by the National Dose Registry, blinded from case status. Person-years at risk of leukemia were stratified by exposure category, calendar period of employment, and attained age at risk. Poisson regression was used to model the risk (RR) of incident leukemia at increasing levels of cumulative gamma radiation exposure, adjusting for calendar period and attained age. Results Between 1969 and 2005, 116 incident cases of leukemia were identified. On average, these miners were employed for 4.4 years with a mean cumulative dose of 5.25 millisieverts (mSv). With exposure lagged by 2 years, preliminary analyses showed that when compared to the referent group (0 mSv), those with >30 mSv of cumulative gamma dose had a non-statistically significant increase in the risk of leukemia diagnosis (RR=2.04, 95% CI: 0.93, 4.51) with increasing, linear trend (p=0.08). Conclusions Although our results did not show a statistically significant relationship between gamma radiation and leukemia incidence, it is likely due to low statistically power. Future work may include pooling the Ontario Uranium Miners cohort with other similar cohorts to better quantify the potential associated risks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".