Effects of different pharmacologic smoking cessation treatments on body weight changes and success rates in patients with nicotine dependence: A network meta‐analysis
Bibliographic record
Abstract
Smoking cessation is a public health priority to reduce smoking-related morbidity and mortality. However, weight gain is a known primary reason for not trying to quit smoking. The aim of the current study was to investigate differences in weight gain associated with different pharmacological smoking cessation interventions. Randomized controlled trials (RCTs) that reported weight gain related to pharmacologic treatments for smoking cessation were analysed using network meta-analysis with a random effects model. Thirty-one RCTs with 5650 participants were included. Ten drugs and 22 regimens were identified. Nicotine patches plus fluoxetine, topiramate with/without nicotine patches, nicotine patches plus methylphenidate, nicotine spray/gum/lozenges, high-dose nicotine patches (42 mg/21 mg), naltrexone with/without nicotine patches, or bupropion with/without nicotine patches were associated with less weight gain than the placebo/control arm. Nicotine patches plus fluoxetine were associated with the least weight gain of all smoking cessation treatments. In addition, the nicotine patch plus topiramate and nicotine inhaler was associated with the best success rate and the least dropout rate, respectively. Overall, the nicotine patch 14 mg plus fluoxetine 40 mg, nicotine patch 14 mg plus fluoxetine 20 mg, and topiramate 200 mg would be the three best pharmacologic treatments based upon both weight gain effect and success rate.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".