Efficacy and safety of e-cigarette use for smoking cessation: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Background Quitting smoking substantially reduces the risk of developing cardiovascular disease. Smokers are increasingly turning to electronic cigarettes (e-cigarettes) to help them quit smoking combustible cigarettes. However, the efficacy and safety of e-cigarettes for smoking cessation remain controversial. Purpose To examine the efficacy and safety of e-cigarette use for smoking cessation. Methods We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing nicotine and non-nicotine e-cigarettes (alone, or in combination with behavioural and/or pharmacological interventions) to conventional smoking cessation therapies (pharmacological, behavioural, or both). We systematically searched MEDLINE, Embase, and the Cochrane Libraries. Inclusion was restricted to RCTs that reported biochemically validated abstinence at six months or greater. The primary comparison was nicotine e-cigarettes versus any (non-e-cigarette) conventional smoking cessation therapy. Count data were pooled using DerSimonian and Laird random-effects models with inverse variance weighting to obtain relative risks (RRs) and 95% confidence intervals (CIs). Results A total of six RCTs (n=3,401) were included, with maximum follow-up durations ranging from six to 12 months. Compared with conventional smoking cessation therapies, point estimates favoured nicotine e-cigarettes for the most rigorous criterion of abstinence, continuous abstinence, and point prevalence abstinence, although findings were inconclusive due to wide 95% CIs (Table 1). Similar trends were observed when comparing non-nicotine e-cigarettes to traditional smoking cessation therapies. Compared with non-nicotine e-cigarettes, nicotine e-cigarettes increased abstinence as defined by the most rigorous criterion. Although there was a signal suggesting a potential increased risk of respiratory adverse events with nicotine e-cigarettes versus traditional therapies, the 95% CI is wide. Conclusion E-cigarettes appear to be more efficacious than conventional nicotine pharmacotherapies and behavioural smoking cessation therapies. Although currently available safety data are inconclusive, e-cigarettes may be considered for smoking cessation given the established long-term health consequences of continued smoking. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Mr. Levett is supported by an Ivan Racheff Scholarship, funded through the McGill University Faculty of Medicine and Health Sciences Research Bursary Program. Dr. Filion is supported by a Senior Research Scholar award from the Fonds de recherche du Québec – Santé and a William Dawson Scholar award from McGill University.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".