Work-Related Factors and Lung Cancer Survival: A Population-Based Study in Switzerland (1990–2014)
Bibliographic record
Abstract
While previous Swiss studies have demonstrated differences in lung cancer mortality between occupational groups, no estimates are available on the association of occupation-related factors with lung cancer survival. This study aimed at determining whether occupation or work-related factors after diagnosis affect lung cancer survival. We used cancer registry records to identify lung cancer patients diagnosed between 1990 and 2014 in western Switzerland (n = 5773) matched with the Swiss National Cohort. The effect of occupation, the skill level required for the occupation, and the socio-professional category on 5-year lung cancer survival was assessed using non-parametric and parametric methods, controlling for histological type and tumour stage. We found that the net survival varied across skill levels and that the lowest skill level was associated with worse survival in both men and women. In the parametric models with minimal adjustment, we identified several occupational groups at higher risk of mortality compared to the reference category, particularly among men. After adjustment for histological type of lung cancer and tumour stage at diagnosis, most hazard ratios remained higher than 1, though non-statistically significant. Compared to top managers and self-employed workers, workers in paid employment without specific information on occupation were identified as the most at-risk socio-professional category in nearly all models. As this study was conducted using a relatively small sample and limited set of covariates, further studies are required, taking into account smoking habits and administrated cancer treatments. Information on return to work and working conditions before and after lung cancer diagnosis will also be highly valuable for analysing their effect on net lung cancer survival in large nationwide or international studies. Such studies are essential for informing health and social protection systems, which should guarantee appropriate work conditions for cancer survivors, beneficial for their quality of life and survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".