Breast Cancer Risk by Occupation in Females and Males in Ontario, Canada: Results from the Occupational Disease Surveillance System (ODSS), 1983-2016
Why this work is in the frame
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Bibliographic record
Abstract
Background: While breast cancer is one of the most commonly diagnosed cancers among women, it accounts for fewer than 1% of cancer cases in men worldwide. Few prior studies have been able to study breast cancer in working men. This study uses data from the recently established Occupational Disease Surveillance System (ODSS) to examine risk of breast cancer in both women and men across different occupation groups.Methods: The ODSS was established through the linkage of existing administrative data and contains information on 2,190,246 Ontario workers (1983-2016). Workers were followed up for breast cancer diagnosis in the Ontario Cancer Registry (OCR). Cox-proportional hazard models were used to calculate age-adjusted hazard ratios (HR) and 95% confidence intervals (CI).Results: A total of 17, 865 and 492 breast cancer cases were identified in working women and men, respectively. Across both sexes, statistically significant (p<0.05) elevated risks were observed in management (w: HR 1.57, 95% CI 1.42-1.73; m: HR 2.41, 95% CI 1.24-4.66), administrative and clerical (w: HR 1.16, 95% CI 1.11-1.21; m: HR 1.56, 95% CI 1.13-2.13), and teaching occupations (w: HR 1.49, 95% CI 1.41-1.59; m: HR 2.82, 95% CI 1.40-5.66). Other statistically significant elevated risks were observed in social sciences, nursing and other health, transport and equipment operating, and sales commodity occupations for both sexes.Conclusions: Similar findings were found in women and men that warrant further investigation into job-related factors, such as sedentary behaviour, stress, shift work, and for some occupations, radiation exposure. The findings from this study, if validated in other study samples, may help focus breast cancer prevention and education efforts for both females and males.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it