Lung cancer risk among cooks and kitchen workers in a pooled analysis of case-control studies in Europe and Canada
Bibliographic record
Abstract
Objectives Several epidemiologic studies indicate an increased risk of lung cancer among cooks but it is not known whether this is caused by cigarette smoking or by occupational exposure to carcinogens. Emissions from high-temperature frying have been classified by the IARC as probably carcinogenic to humans. Methods We used data from the SYNERGY project with pooled information on lifetime work histories and tobacco smoking from 13 176 lung cancer cases and 16 129 controls from 11 case-control studies in Europe and Canada. There were 704 persons (405 men, 299 women) who had ever worked as a cook or kitchen worker (based on ISCO-68), among them 340 cases and 364 controls. ORs and 95% CIs were estimated by unconditional logistic regression, adjusted for study, age, sex, smoking, and ever employment in an occupation with established lung cancer risk. Results Occupation as a cook or kitchen worker was associated with an increased lung cancer risk before (OR 1.20, 95% CI 1.03 to 1.40) but not after (OR 1.01, 95% CI 0.86 to 1.20) controlling for smoking habits. There was no significant exposure-response relationship in terms of work duration, and no significant heterogeneity in lung cancer risk among cooks across studies. It was not possible to separate cooks from other kitchen workers. Conclusions Working as a cook or kitchen worker was not associated with an increased risk of lung cancer. However, the possible risk by cooking fumes cannot be ruled out. Misclassification of exposure may have biased our results towards the null.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".