Post-Cessation Weight Gain Across Smoking Cessation Therapies: A Review of Secondary Analyses from the ZESCA, EVITA, and E3 Trials
Bibliographic record
Abstract
Background: Post-cessation weight gain is a barrier to smoking abstinence, yet evidence on the role of e-cigarettes in mitigating this remains limited. Objective: To examine weight-related effects of e-cigarettes in comparison with established cessation methods. Methods: We reviewed data from three cessation trials we conducted between 2005 and 2020. In ZESCA and EVITA, patients were randomized to bupropion or varenicline versus placebo. In the E3 trial, participants were randomized to counseling alone or with nicotine or non-nicotine e-cigarettes. Post hoc analyses assessed weight at 52 weeks for bupropion and varenicline, and 12 weeks for e-cigarettes. Synthesis: Abstinent individuals showed significant weight gain from baseline across the trials. In ZESCA and EVITA, abstinent participants gained more weight than intermittent and persistent smokers at 52 weeks (ZESCA: 4.8 vs. 2.0 vs. 3.0 kg, EVITA: 4.8 vs. 2.0 vs. −0.7 kg, respectively). Abstinent individuals gained more weight than persistent smokers (ZESCA: 3.4 kg, EVITA: 5.5 kg). In the E3 trial, abstinent participants with nicotine e-cigarettes gained more weight than those using non-nicotine e-cigarettes or counseling at 12 weeks (2.7 vs. 2.3 vs. 2.1 kg, respectively). Conclusions: Abstinent individuals experienced significant weight gain regardless of cessation treatment. Long-term effects of e-cigarettes on weight remain unclear.
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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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".