Alcohol and lung cancer risk among never smokers: A pooled analysis from the international lung cancer consortium and the SYNERGY study
Bibliographic record
Abstract
It is not clear whether alcohol consumption is associated with lung cancer risk. The relationship is likely confounded by smoking, complicating the interpretation of previous studies. We examined the association of alcohol consumption and lung cancer risk in a large pooled international sample, minimizing potential confounding of tobacco consumption by restricting analyses to never smokers. Our study included 22 case-control and cohort studies with a total of 2548 never-smoking lung cancer patients and 9362 never-smoking controls from North America, Europe and Asia within the International Lung Cancer Consortium (ILCCO) and SYNERGY Consortium. Alcohol consumption was categorized into amounts consumed (grams per day) and also modelled as a continuous variable using restricted cubic splines for potential non-linearity. Analyses by histologic sub-type were included. Associations by type of alcohol consumed (wine, beer and liquor) were also investigated. Alcohol consumption was inversely associated with lung cancer risk with evidence most strongly supporting lower risk for light and moderate drinkers relative to non-drinkers (>0-4.9 g per day: OR = 0.80, 95% CI = 0.70-0.90; 5-9.9 g per day: OR = 0.82, 95% CI = 0.69-0.99; 10-19.9 g per day: OR = 0.79, 95% CI = 0.65-0.96). Inverse associations were found for consumption of wine and liquor, but not beer. The results indicate that alcohol consumption is inversely associated with lung cancer risk, particularly among subjects with low to moderate consumption levels, and among wine and liquor drinkers, but not beer drinkers. Although our results should have no relevant bias from the confounding effect of smoking we cannot preclude that confounding by other factors contributed to the observed associations. Confounding in relation to the non-drinker reference category may be of particular importance.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".