Is occupational prestige an independent risk factor for lung cancer?
Bibliographic record
Abstract
Background We studied the association between lung cancer and the level of time-weighted average occupational social prestige as well as occupational prestige’s lifetime trajectory. Methods We included 11,433 male cases and 14,147 male control subjects from the international pooled SYNERGY case-control study. Each job was translated into an occupational social prestige score by applying Treiman’s Standard International Occupational Prestige Scale (SIOPS) and categorized as low, medium, and high prestige (reference). We calculated odds ratios (OR) and 95% confidence intervals (CI), adjusting for study center, age, smoking, ever employment in a job with known lung carcinogen exposure, and education. Trajectories in SIOPS categories from first to last and first to longest job were defined as consistent, downward, or upward. We conducted several sensitivity analyses to assess the robustness of our results. Results In the fully adjusted models we observed increased lung cancer risk estimates for men with medium (OR = 1.23; 95% CI 1.13–1.33) and low occupational prestige (OR = 1.44; 95% CI 1.32–1.57). Adjustment for smoking habits and education attenuated these associations. Risk estimates for low prestige were elevated among non-smokers (OR = 1.65; 95% CI 1.14–2.40) and subjects working in white collar occupations (OR = 1.30, 95% CI 1.13–1.49). Subjects with a college/university degree did not show elevated risk estimates. Associations with downward prestige trajectories were only slightly elevated. Conclusions Our results indicate an independent association between occupational prestige and lung cancer. Key messages Education and smoking were important risk factors, but did not fully explain the association between social occupational prestige and lung cancer Occupational exposures to carcinogenic agents and loss of occupational prestige over the work life contributed only marginally to the observed associations
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".