Time-dependent effect of intensity of smoking and of occupational exposure to asbestos on the risk of lung cancer: results from the ICARE case–control study
Bibliographic record
Abstract
OBJECTIVE: To estimate the impact of intensity of both smoking and occupational exposure to asbestos on the risk of lung cancer throughout the whole exposure history. METHODS: Data on 2026 male cases and 2610 male controls came from the French ICARE (Investigation of occupational and environmental causes of respiratory cancers) population-based, case-control study. Lifetime smoking history and occupational history were collected from standardised questionnaires and face-to-face interviews. Occupational exposure to asbestos was assessed using a job exposure matrix. The effects of annual average daily intensity of smoking (reported average number of cigarettes smoked per day) and asbestos exposure (estimated average daily air concentration of asbestos fibres at work) were estimated using a flexible weighted cumulative index of exposure in logistic regression models. RESULTS: Intensity of smoking in the 10 years preceding diagnosis had a much stronger association with the risk of lung cancer than more distant intensity. By contrast, intensity of asbestos exposure that occurred more than 40 years before diagnosis had a stronger association with the risk of lung cancer than more recent intensity, even if intensity in the 10 years preceding diagnosis also had a significant effect. CONCLUSION: Our results illustrate the dynamic of the effect of intensity of both smoking and occupational exposure to asbestos on the risk of lung cancer. They confirm that the timing of exposure plays an important role, and suggest that standard analytical methods assuming equal weights of intensity over the whole exposure history may be questionable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".