Occupational prestige, social mobility and the association with lung cancer in men
Bibliographic record
Abstract
BACKGROUND: The nature of the association between occupational social prestige, social mobility, and risk of lung cancer remains uncertain. Using data from the international pooled SYNERGY case-control study, we studied the association between lung cancer and the level of time-weighted average occupational social prestige as well as its lifetime trajectory. METHODS: We included 11,433 male cases and 14,147 male control subjects. Each job was translated into an occupational social prestige score by applying Treiman's Standard International Occupational Prestige Scale (SIOPS). SIOPS scores were categorized as low, medium, and high prestige (reference). We calculated odds ratios (OR) with 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 subgroup and sensitivity analyses to assess the robustness of our results. RESULTS: 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). Although adjustment for smoking and education reduced the associations between occupational prestige and lung cancer, they did not explain the association entirely. Traditional occupational exposures reduced the associations only slightly. We observed small associations with downward prestige trajectories, with ORs of 1.13, 95 % CI 0.88-1.46 for high to low, and 1.24; 95 % CI 1.08-1.41 for medium to low trajectories. CONCLUSIONS: Our results indicate that occupational prestige is independently associated with lung cancer among men.
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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 | 0.001 |
| 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) | 0.001 | 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".