Alcohol and Tobacco Use Prediagnosis and Postdiagnosis, and Survival in a Cohort of Patients with Early Stage Cancers of the Oral Cavity, Pharynx, and Larynx
Bibliographic record
Abstract
As more people begin to survive first cancers, there is an increased need for science-based recommendations to improve survivorship. For survivors of head and neck cancer, use of tobacco and alcohol before diagnosis predicts poorer survival; however, the role of continuing these behaviors after diagnosis on mortality is less clear, especially for more moderate alcohol consumption. Patients (n = 264) who were recent survivors of early stage head and neck cancer were asked to retrospectively report their tobacco and alcohol histories (before diagnosis), with information prospectively updated annually thereafter. Patients were followed for an average of 4.2 years, with 62 deaths observed. Smoking history before diagnosis dose-dependently increased the risk of dying; risks reached 5.4 [95% confidence interval (95% CI), 0.7-40.1] among those with >60 pack-years of smoking. Likewise, alcohol history before diagnosis dose-dependently increased mortality risk; risks reached 4.9 (95% CI, 1.5-16.3) for persons who drank >5 drinks/d, an effect explained by beer and liquor consumption. After adjusting for prediagnosis exposures, continued drinking (average of 2.3 drinks/d) postdiagnosis significantly increased risk (relative risk for continued drinking versus no drinking, 2.7; 95% CI, 1.2-6.1), whereas continued smoking was associated with nonsignificantly higher risk (relative risk for continued smoking versus no smoking, 1.8; 95% CI, 0.9-3.9). Continued drinking of alcoholic beverages after an initial diagnosis of head and neck cancer adversely affects survival; cessation efforts should be incorporated into survivorship care of these patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".