Lung cancer and occupational social status: The synergy study
Bibliographic record
Abstract
Introduction Several studies associated low socioeconomic status (SES) with lung cancer. However, many were not able to consider smoking behaviour appropriately. We took advantage of the international SYNERGY study of pooled case-control studies with detailed information of smoking habits and the occupational history to study the association between lung cancer and occupationally derived SES. Methods Twelve case-control studies from Europe and Canada were included. We estimated SES based on the subjects' complete occupational histories using the International Socio-Economic Index of Occupational Status (ISEI) and the European Socio-economic Classification (ESeC). ISEI was categorised into four equidistant categories comprising the same number of codes and, secondly, according to quartiles of the sex-specific score distribution among control subjects. We calculated odds ratios (OR) and 95% confidence intervals (CI) by unconditional logistic regression, adjusting for age, study, and smoking behaviour, and stratified by sex. Subgroup analyses by lung cancer histological subtype, study region, birth cohort, education, and occupational exposure to known lung carcinogens were also carried out. Result We included 17 021 cases and 20 885 control subjects into the final analysis. There was a strongly elevated association of lung cancer with low SES in the analysis adjusted for age and study. Adjustment for smoking attenuated the associations, however, a social gradient with lung cancer persisted. Comparing the lowest vs highest SES category in men yielded: ISEI OR=1.84 (95% CI: 1.61 to 2.09) and ESeC OR=1.53 (95% CI: 1.44 to 1.63). ORs for women were slightly lower: ISEI OR=1.54 (95% CI: 1.20 to 1.98) and ESeC OR=1.34 (95% CI: 1.19 to 1.52). Discussion Low SES remained an independent risk factor for lung cancer even after controlling for smoking habits.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".