O6B.2 Cancer risk by ore type in a mixed miners cohort
Bibliographic record
Abstract
Background and objectives Mining may involve exposure to many carcinogens, including respirable crystalline silica (RSC), diesel engine exhaust (DEE), nickel (Ni), chromium (Cr), radon (Rn), and arsenic (As), which vary by ore being mined. The province of Ontario, Canada has a diverse mining sector with associated exposures including gold (RSC/DEE/As/Cr), uranium (RSC/DEE/Rn), and nickel-copper (DEE/Ni), and other ores (RSC/DEE). The study aim was to examine the risk of cancer by ore type in a mixed mining cohort. Methods From 1928–1987 workers in the Ontario minerals industry were required to undergo an annual physical examination and chest x-ray, as well as record their mining work history in order to receive certification. Data from these exams was used to create the Mining Master File (MMF) cohort. Cancers were identified through linkage of the MMF with the Ontario Cancer Registry (1964–2017). Cancer risk among miners was compared to provincial rates using Standardized Incidence Ratios (SIR); internal analyses were conducted using Poisson regression. Results Individuals who died or were lost before 1964, had missing or invalid data, or employment of less than two weeks were excluded. Too few women (n=161) were available for analysis. In total, 61 397 men were included in the analysis. Gold miners had excesses of lung (SIR=1.30, 95%CI=1.23–1.38) and nasopharyngeal cancer (SIR=2.34, 95%CI=1.39–3.70). Uranium miners had excesses of lung (SIR=1.57, 95%CI=1.45–1.70), bladder (SIR=1.20, 95%CI=1.02–1.40), and bone (SIR=2.45, 95%CI=1.30–4.19) cancers. Nickel-copper miners had excesses of lung (SIR=1.13, 95%CI=1.08–1.19), bone (SIR=2.02, 95%CI=1.32–2.96), and sinonasal cancer (SIR=1.73, 95%CI=1.12–2.56). Conclusions Increased risks for specific cancers were observed among people who mined many different ore types. Most of the associations were as expected, but several (e.g., bone cancers) will undergo further investigation. Future analyses will examine the impact of combined exposures among miners of multiple ore types.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".