Lung cancer risk in relation to jobs held in a nationwide case–control study in Iran
Bibliographic record
Abstract
BACKGROUND: Globally, lung cancer is the most frequent occupational cancer, but the risk associated with the occupations or occupational environment in Iran is not clear. We aimed to assess occupations with the risk of lung cancer. METHODS: We used the IROPICAN nationwide case-control study data including 658 incident lung cancer cases and 3477 controls. We assessed the risk of lung cancer in relation to ever working in major groups of International Standard Classification of Occupations, high-risk occupations for lung cancer and duration of employment and lung cancer subtype among construction workers and farmers while controlling for cigarette smoking and opium consumption. We used unconditional regression logistic models to estimate ORs for the association between increased lung cancer risk and occupations. RESULTS: We observed elevated ORs for lung cancer in male construction workers (OR=1.4; 95% CI: 1.0 to 1.8), petroleum industry workers (OR=3.2; 95% CI: 1.1 to 9.8), female farmers (OR=2.6; 95% CI: 1.3 to 5.3) and female bakers (OR=5.5; 95% CI: 1.0 to 29.8). A positive trend by the duration of employment was observed for male construction workers (p< 0.001). Increased risk of squamous cell carcinoma was observed in male construction workers (OR=1.9; 95% CI: 1.2 to 3.0) and female farmers (OR=4.3; 95% CI: 1.1 to 17.2), who also experienced an increased risk of adenocarcinoma (OR=3.8; 95% CI: 1.4 to 9.9). DISCUSSION: Although we observed associations between some occupations and lung cancer consistent with the literature, further studies with larger samples focusing on exposures are needed to better understand the occupational lung cancer burden in Iran.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".