Comparative Effectiveness of Electronic Cigarettes and Pharmacotherapy for Smoking Cessation: A Systematic Review and Bayesian Network Meta-analysis of Randomized Trials
Bibliographic record
Abstract
Abstract Background: Electronic cigarettes (e-cigarettes) are commonly used as an aid for smoking cessation, but their comparative effectiveness versus conventional pharmacotherapies remains uncertain. This study aimed to perform a network meta-analysis of randomized controlled trials (RCTs) evaluating the effectiveness of e-cigarettes compared to conventional pharmacotherapies for smoking cessation. Methods: We conducted a systematic review and Bayesian network meta-analysis of RCTs. We searched Embase, PsychInfo, Cochrane CENTRAL, and Web of Science for RCTs comparing approved or e-cigarettes to standard care, no treatment, or behavioral treatment. The primary outcome was biochemically confirmed continuous smoking cessation. We used Bayesian random-effects network meta-analysis for data synthesis. Results: A total of 309 RCTs including 143,823 patients were analyzed. E-cigarettes increase smoking cessation compared to placebo (OR 2.50, 95% CrI: 2.00 to 3.20) (high certainty). E-cigarettes probably increase smoking cessation compared to control interventions (OR 2.50, 95% CrI: 2.00 to 3.13) (moderate certainty) and compared to nicotine replacement therapy (NRT) (OR 1.39, 95% CrI: 1.11 to 1.72) (moderate certainty). E-cigarettes probably increase smoking cessation compared to bupropion (OR 1.43, 95% CrI: 1.11 to 1.82) (moderate certainty). The effect of e-cigarettes compared to bupropion combined with NRT is very uncertain (OR 1.27, 95% CrI: 0.86 to 1.82) (very low certainty). E-cigarettes probably reduce smoking cessation compared to the combination of e-cigarettes with NRT (OR 0.60, 95% CrI: 0.35 to 1.11) (moderate certainty). E-cigarettes may reduce smoking cessation compared to the combination of e-cigarettes with varenicline (OR 0.46, 95% CrI: 0.15 to 1.16) (low certainty). Compared to varenicline alone, e-cigarettes may not significantly improve smoking cessation rates (OR 0.92, 95% CrI: 0.68 to 1.19) (low certainty). E-cigarettes may also not significantly differ in effectiveness compared to varenicline combined with bupropion (OR 0.66, 95% CrI: 0.36 to 1.25) (low certainty) or varenicline combined with NRT (OR 0.67, 95% CrI: 0.40 to 1.14) (low certainty). Conclusion: Our network meta-analysis suggests that e-cigarettes increase smoking cessation compared to placebo, and probably compared to control interventions, NRT, and bupropion. However, the effectiveness of e-cigarettes compared to combination therapies, including NRT and varenicline, remains uncertain.
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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 | 0.061 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.047 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".