The effects of pharmacological interventions on smoking cessation in people with alcohol dependence: A systematic review and meta‐analysis of nine randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Pharmacotherapies are widely used for smoking cessation. However, their efficacy for people with alcohol dependence remains unclear. OBJECTIVE: This study aimed to explore the effects of pharmacotherapies on smoking cessation for people with alcohol dependence. METHODS: Five electronic databases were searched in January 2021 for randomized controlled trials (RCTs) reporting the use of pharmacotherapies to promote smoking cessation in people with alcohol dependence. The risk of bias was assessed using the Cochrane tool. RevMan version 5.3 was used to perform meta-analyses of the changes in smoking behaviour, and the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach was used to assess the certainty of the evidence. RESULTS: The search identified nine RCTs involving 908 smokers with alcohol dependence; eight were published in the USA and one in Canada. The risk of bias was low in three studies and unclear in the remaining six. The meta-analysis results showed that, compared with the placebo group, Varenicline had a significant effect on short-term smoking cessation (three RCTs, odds ratio [OR] = 6.27, 95% confidence interval [CI]: [2.49, 15.78], P < .05, very low certainty). Naltrexone had no significant effect on smoking cessation in short-term or long-term observations (three RCTs, OR = 0.99, 95% CI: [0.54, 1.81], P = .97, moderate certainty), and Topiramate had no significant effects (two RCTs, OR = 1.56, 95% CI: [0.67, 3.46], P > .05, low certainty). Only one trial reported that Bupropion did not affect smoking cessation. CONCLUSION: Varenicline may promote smoking cessation in people with alcohol dependence. However, Naltrexone, Topiramate and Bupropion have no clear effect on increasing smoking abstinence among drinkers. The small number of studies and the low certainty of evidence indicate that the results should be interpreted cautiously.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.047 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".