Effect of Radiation Dose Rate on Circulatory Disease Mortality among Nuclear Workers: Reanalysis of Hanford Data
Bibliographic record
Abstract
The excess relative risk (ERR) of mortality for circulatory disease among nuclear workers was reanalyzed by taking into consideration the annual dose as the dose rate using publicly available epidemiological data of the Hanford site dedicated to the cohort study of nuclear workers in the US, the UK, and Canada. Values of the dose rate (cut-points) were chosen at 2 mSv y intervals from 2 to 40 mSv y, and risk estimates were made for 32,988 workers, considering the doses accumulated below and above each cut-point to have different effects. Similarly to that in the previous study for cancer by Sasaki et al., examinations of sensitivity analysis were also carried out for different risk models, lag periods, and impacts of adjusting the monitoring period to find the effect of the dose rate. As a result, emergence of a statistically significant difference between βL1, which is the ERR for the doses accumulated below the specified cut-point, and βH1, which is the ERR for that above the specified cut-point, was observed for cut-point of the dose rate of 4, 6, 34, 36, and 38 mSv y. While statistically negative values were estimated for βL1 (4 and 6 mSv y) and for βH1 (34, 36, and 38 mSv y), the overall relationship between the ERR and the cut-point of the dose rate was found to be similar to that obtained by the analysis of the mortality for all cancers excluding leukemia.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".