Alcohol history and smoking cessation in nicotine replacement therapy, bupropion sustained release and varenicline trials: a review
Bibliographic record
Abstract
AIMS: We conducted a review of published reports of smoking cessation pharmacotherapy trials in order to address the following: (i) the generalizability of findings to smokers with a history of alcohol problems; (ii) the extent to which alcohol use affects smoking cessation overall and the efficacy of pharmacotherapy specifically and (iii) the effect of smoking cessation on alcohol use. METHODS: We located published reports of nicotine replacement therapy (NRT), bupropion sustained release (SR) and varenicline clinical trials using an approach based on prior Cochrane reviews. The reports were searched for alcohol-related inclusion/exclusion criteria and for findings related to alcohol. RESULTS: The present review included 212 published reports from 149 trials. Alcohol-related exclusion criteria appeared frequently (41.6% of trials)--45/125 NRT trials (36%), 15/22 bupropion SR trials (68.2%) and 3/3 varenicline trials--and most commonly involved exclusion of participants with either current or recent alcohol problems. Most studies failed to provide any baseline alcohol-related characteristics. Eleven trials reported on the relationship between alcohol history and likelihood of smoking cessation. In the majority of these studies, smokers with a past history of alcohol problems were not at a disadvantage, although contrary findings exist. Only two studies examined the potential influence of smoking cessation on alcohol use. CONCLUSIONS: Smokers with alcohol problems, particularly those with current or recent problems, are underrepresented in studies of approved pharmacotherapy for smoking cessation. Future trials should assess alcohol use at baseline and during treatment and examine reciprocal influences between alcohol consumption and smoking cessation.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".