Smoking cessation in individuals who use vaping as compared with traditional nicotine replacement therapies: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Despite the aggressive marketing of electronic nicotine device systems (ENDS) as smoking cessation tools, the evidence of their effectiveness is mixed. We conducted a systematic review of randomised controlled trials to determine the effect of ENDS on cigarette smoking cessation, as compared with other types of nicotine replacement therapies (NRT). DESIGN: Systematic review and meta-analysis using the Grading of Recommendations Assessment, Development and Evaluation approach. DATA SOURCES: MEDLINE, Embase, the CENTRAL Trials Registry of the Cochrane Collaboration using the Ovid interface, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform trials registries were searched through 17 June 2020. ELIGIBILITY CRITERIA FOR STUDIES: Randomised controlled trials in which any type of ENDS was compared with any type of NRT, in traditional cigarette users. DATA EXTRACTION AND SYNTHESIS: The primary outcome was smoking cessation, defined as abstinence from traditional cigarette smoking for any time period, as reported in each included study, regardless of whether abstinence is self-reported or biochemically validated. Secondary outcomes included smoking reduction, harms, withdrawal and acceptance of therapy. A random-effect model was used, and data were pooled in meta-analyses where appropriate. RESULTS: Six studies were retained from 270. Most outcomes were judged to be at high risk of bias. The overall quality of evidence was graded as 'low' or 'very low'. Pooled results showed no difference in smoking cessation (rate ratio (RR) 1.42, 95% CI 0.97 to 2.09), proportion of participants reducing smoking consumption (RR 1.25, 95% CI 0.79 to 1.98), mean reduction in cigarettes smoked per day (mean difference 1.11, 95% CI -0.41 to 2.63), or harms (RR 0.96, 95% CI 0.76 to 1.20), between groups. CONCLUSION: We found no difference in smoking cessation, harms and smoking reduction between e-cigarette and NRT users. However, the quality of the evidence was low. Further research is needed before widespread recommendations are made with regard to the use of ENDS. PROSPERO REGISTRATION NUMBER: , 2020; CRD42020169416.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.055 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 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".