The effects of alcohol use on smoking cessation treatment with nicotine replacement therapy: An observational study
Bibliographic record
Abstract
INTRODUCTION: Concurrent users of tobacco and alcohol are at greater risk of harm than use of either substance alone. It remains unclear how concurrent tobacco and alcohol use affects smoking cessation across levels of alcohol use and related problems. This study assessed the relationship between smoking cessation and levels of alcohol use problems. METHODS: 59,018 participants received nicotine replacement therapy through a smoking cessation program. Alcohol use and related symptoms were assessed using the Alcohol Use Disorders Identification Test (AUDIT-10) and the AUDIT-Concise (AUDIT-C). The primary outcome was 7-day point prevalence cigarette abstinence (PPA) at 6-month follow-up. We evaluated the association between alcohol use (and related problems) and smoking cessation using descriptive methods and mixed-effects logistic regression. RESULTS: 7-day PPA at 6-months was lower in groups meeting hazardous alcohol consumption criteria, with the lowest probability of smoking abstinence observed in the highest risk group. The probability of successful tobacco cessation fell with increasing levels of alcohol use and related problems. Adjusted predicted probabilities were 30.3 (95 % CI = 29.4, 31.1) for non-users, 30.2 (95 % CI = 29.4, 31.0) for low-risk users, 29.0 (95 % CI = 28.1, 29.9) for those scoring below 8 on the AUDIT-10, 27.3 (95 % CI = 26.0, 28.6) for those scoring 8-14, and 24.4 (95 % CI = 22.3, 26.5) for those scoring 15 or higher. CONCLUSION: Heavy, hazardous alcohol use is associated with lower odds of successfully quitting smoking compared to low or non-use of alcohol. Targeting alcohol treatment to this group may improve tobacco cessation outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".