Alcohol consumption and upper aerodigestive tract squamous cell carcinoma: evidence from 28 prospective cohorts
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the association between alcohol consumption and squamous cell cancers of the upper aerodigestive tract (UADT), using data from 28 cohorts within the Pooling Project of Prospective Studies of Diet and Cancer (DCPP). METHODS: Individual-level data from 2 365 437 participants were pooled. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox models to quantify the association between alcohol consumption (g/day) and UADT cancer risk, adjusting for potential confounders. Analyses were conducted by sex, smoking status, geographic region, and alcoholic beverages. RESULTS: Over a median follow-up of 15.5 years, 6903 UADT cancer cases were identified. Alcohol consumption was positively associated with UADT cancer risk overall. Even at intakes as low as 5-<15 g/day, the HR estimate was 1.12 (95% CI = 1.03 to 1.21) compared with the reference group (0.1-<5 g/day). The HR10 g/day (95% CI) was 1.16 (1.14 to 1.18) for women and 1.12 (1.11 to 1.13) for men (Pheterogeneity < .0001). HR10 g/day estimates were 1.14 (1.13 to 1.15) in current, 1.10 (1.09 to 1.12) in former, and 1.15 (1.12 to 1.18) in never smokers. Consistent UADT HR10 g/day estimates were observed across all beverage types. HR10 g/day estimates varied across geographic regions, with HR10 g/day (95% CI) equal to 1.15 (1.14 to 1.17) in Europe-Australia, 1.13 (1.11 to 1.15) in Asia, and 1.11 (1.09 to 1.12) in North America (Pheterogeneity < .0001). CONCLUSION: Alcohol consumption was associated with UADT cancer risk, irrespective of smoking status or beverage type. However, due to differential baseline risks, alcohol is expected to impact the UADT cancer burden more in smokers than never smokers. These findings support public health strategies to reduce alcohol consumption.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".