Estimating 10-year risk of lung and breast cancer by occupation in Switzerland
Bibliographic record
Abstract
Introduction: Lung and breast cancer are important in the working-age population both in terms of incidence and costs. The study aims were to estimate the 10-year risk of lung and breast cancer by occupation and smoking status and to create easy to use age-, and sex-specific 10-year risk charts. Methods: New lung and breast cancer cases between 2010 and 2014 from all 5 cancer registries of Western Switzerland, matched with the Swiss National Cohort were used. The 10-year risks of lung and breast cancer by occupational category were estimated. For lung cancer, estimates were additionally stratified by smoking status using data on smoking prevalence from the 2007 Swiss Health Survey. Results: The risks of lung and breast cancer increased with age and were the highest for current smokers. Men in elementary professions had a higher 10-year risk of developing lung cancer compared to men in intermediate and managerial professions. Women in intermediate professions had a higher 10-year risk of developing lung cancer compared to elementary and managerial professions. However, women in managerial professions had the highest risk of developing breast cancer. Discussion: The 10-year risk of lung and breast cancer differs substantially between occupational categories. Smoking creates greater changes in 10-year risk than occupation for both sexes. The 10-year risk is interesting for both patients and professionals to inform choices related to cancer risk, such as screening and health behaviors. The risk charts can also be used as public health indicators and to inform policies to protect workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".