S06-4 Addressing non-radiologic exposures in studies of uranium miners in ontario, canada
Bibliographic record
Abstract
We recently completed an extended follow-up of a cohort of 29,000 uranium miners, employed in Ontario (a province in central Canada) mines (1954–1996). The primary objective was to examine the risks of lung cancer associated with radon exposure. However, uranium miners are also potentially exposed to other work-related lung carcinogens and many miners move between different sub-sectors, due to the cyclical nature of the industry. Therefore, we also undertook analyses to examine the potential impact of these factors on the association between radon and lung cancer. During the follow-up period (1954–2007) there were 1230 fatal lung cancers. A strong dose-response was observed with a relative risk (RR) of 2.32 (95% confidence interval (CI) = 1.72–3.14) for >100 WLM (with 5-year lag). The one departure from a monotonic increase in risk was among very low-exposed workers (RR = 1.43, 95% CI = 1.04–1.95 for >0–1 WLM). Forty-seven silicosis deaths were observed in the cohort (SMR = 19.7, 95% CI = 14.5–26.2). One third of the cohort (9,138 miners) had also worked in gold mining, where arsenic exposure is likely, and had increased risks of both silicosis and lung cancer. However, excesses were also observed among uranium miners never employed in gold mining, and among miners from areas with uranium deposits low in silica content. Many mines converted to diesel engines during the study period, but it was more difficult to assess the impact of this exposure on lung cancer without mine-specific exposure data. This study identified a number of potential exposures, including crystalline silica and diesel engine exhaust both within uranium mines and from other sectors, which could potentially distort the relationship between radon and lung cancer. We suspect that these exposures could be responsible for the excess risk of lung cancer observed among low radon exposed miners, but better indicators of exposure are needed to fully assess these relationships.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".