Bladder cancer risk in relation to occupations held in a nationwide case‐control study in Iran
Bibliographic record
Abstract
Globally, bladder cancer has been identified as one of the most frequent occupational cancers, but our understanding of occupational bladder cancer risk in Iran is less advanced. This study aimed to assess the risk of bladder cancer in relation to occupation in Iran. We used the IROPICAN case-control study data including 717 incident cases and 3477 controls. We assessed the risk of bladder cancer in relation to ever working in major groups of the International Standard Classification of Occupations (ISCO-68) while controlling for cigarette smoking, opium consumption. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CI). In men, decreased ORs for bladder cancer were observed in administrative and managerial workers (OR 0.4; CI: 0.2, 0.9), and clerks (OR 0.6; CI: 0.4, 0.9). Elevated ORs were observed in metal processors (OR 5.4; CI: 1.3, 23.4), and workers in occupations with likely exposure to aromatic amines (OR 2.2; CI: 1.2, 4.0). There was no evidence of interactions between working in aromatic amines-exposed occupations and tobacco smoking or opium use. Elevated risk of bladder cancer in men in metal processors and workers likely exposed to aromatic amines aligns with associations observed outside Iran. Other previously confirmed associations between high-risk occupations and bladder cancer were not observed, possibly due to small numbers or lack of details on exposure. Future epidemiological studies in Iran would benefit from the development of exposure assessment tools such as job exposure matrices, generally applicable for retrospective exposure assessment in epidemiological studies.
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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 | 0.000 | 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".