Efficacy and safety of varenicline for vaping cessation: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Although varenicline is efficacious and safe for smoking cessation, its role in vaping cessation remains uncertain. METHODS: We searched MEDLINE, EMBASE, and the Cochrane Library for randomized controlled trials (RCTs) comparing varenicline with placebo for vaping cessation. The primary outcome was the most rigorous criterion of biochemically validated vaping abstinence at maximum follow-up. Secondary outcomes were 7-day point prevalence and continuous abstinence at end of treatment and maximum follow-up. Safety outcomes included any adverse events and serious adverse events. Relative risks (RRs) and 95% confidence intervals (CIs) were estimated using random-effects models. RESULTS: Three RCTs were included, comprising 178 participants randomized to varenicline and 177 to placebo. The mean participant age ranged from 21-54 years, and the proportion of males ranged from 46-51%. Treatment duration was 8-12 weeks, and maximum follow-up was 12-24 weeks. The pooled RR for varenicline versus placebo for vaping abstinence at maximum follow-up was 2.20 (95% CI 0.58-8.36). Varenicline was associated with doubling of 7-day point prevalence abstinence at end of treatment (RR: 2.29; 95% CI 1.21-4.33) and maximum follow-up (RR: 2.22; 95% CI 1.03-4.81). In the two trials reporting continuous abstinence, rates were greater with varenicline than placebo at end of treatment (51% vs 14% and 40% vs 20%) and maximum follow-up (28% vs 7% and 34% vs 17%). Most adverse events were mild and transient; serious adverse events were rare (range: 0-3%). CONCLUSION: Varenicline appears safe and promising for vaping cessation. However, larger RCTs are needed to confirm its long-term efficacy and safety.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".