Cannabis smoking and lung cancer risk: Pooled analysis in the International Lung Cancer Consortium
Why this work is in the frame
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Bibliographic record
Abstract
To investigate the association between cannabis smoking and lung cancer risk, data on 2,159 lung cancer cases and 2,985 controls were pooled from 6 case-control studies in the US, Canada, UK, and New Zealand within the International Lung Cancer Consortium. Study-specific associations between cannabis smoking and lung cancer were estimated using unconditional logistic regression adjusting for sociodemographic factors, tobacco smoking status and pack-years; odds-ratio estimates were pooled using random effects models. Subgroup analyses were done for sex, histology and tobacco smoking status. The shapes of dose-response associations were examined using restricted cubic spline regression. The overall pooled OR for habitual versus nonhabitual or never users was 0.96 (95% CI: 0.66-1.38). Compared to nonhabitual or never users, the summary OR was 0.88 (95%CI: 0.63-1.24) for individuals who smoked 1 or more joint-equivalents of cannabis per day and 0.94 (95%CI: 0.67-1.32) for those consumed at least 10 joint-years. For adenocarcinoma cases the ORs were 1.73 (95%CI: 0.75- 4.00) and 1.74 (95%CI: 0.85-3.55), respectively. However, no association was found for the squamous cell carcinoma based on small numbers. Weak associations between cannabis smoking and lung cancer were observed in never tobacco smokers. Spline modeling indicated a weak positive monotonic association between cumulative cannabis use and lung cancer, but precision was low at high exposure levels. Results from our pooled analyses provide little evidence for an increased risk of lung cancer among habitual or long-term cannabis smokers, although the possibility of potential adverse effect for heavy consumption cannot be excluded.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it