Comparisons of high-dose and combination nicotine replacement therapy, varenicline, and bupropion for smoking cessation: A systematic review and multiple treatment meta-analysis
Bibliographic record
Abstract
AIM: This review compared the effect of high-dose nicotine replacement therapy (NRT) and combinations of NRT for increasing smoking abstinence rates compared to standard-dose NRT patch, varenicline, and bupropion on smoking abstinence. METHODS: Ten electronic databases were searched (up to January 2012) for randomized controlled trials (RCT) of standard-dose (≤ 22 mg) or high-dose nicotine patch therapy (> 22 mg), combination NRT (e.g. nicotine patch + nicotine inhaler), bupropion, and varenicline. Analysis consisted of random-effects pairwise meta-analysis and a Bayesian multiple treatment comparison (MTC). RESULTS: We identified 146 RCTs (65 standard-doses of the nicotine patch (≤ 22 mg); 6 high-dose NRT patch (> 22 mg); 5 high versus standard-dose NRT patch; 5 combination NRT versus inert controls; 6 combination versus single NRT patch; 48 bupropion; and 11 varenicline). The MTC found that all therapies offered treatment benefits at most time points over controls. Combination NRT and higher-dose NRT did not demonstrate consistent effects over other interventions. With the exception of varenicline, the benefits of treatments over standard-dose NRT were not retained in the long term. CONCLUSIONS: All pharmacologic treatments were significantly more effective than inert controls. Varenicline was the only treatment demonstrating effects over other options. These results should be considered in the development of clinical practice guidelines.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".