The Effect of 12-Week e-Cigarette Use on Smoking Abstinence at 1 Year
Bibliographic record
Abstract
BACKGROUND: The current evidence regarding the long-term efficacy of electronic cigarettes (e-cigarettes) for smoking cessation is unclear. OBJECTIVES: The purpose of this study was to assess the efficacy, safety, and tolerability of nicotine and non-nicotine e-cigarettes for smoking cessation in the general population. METHODS: We randomized 376 adults who smoked ≥10 cigarettes/day and were motivated to quit at 17 Canadian sites to 12 weeks of nicotine (15 mg/mL) e-cigarettes (n = 128), non-nicotine e-cigarettes (n = 127), or no e-cigarettes (n = 121). All groups received individual counseling. The primary endpoint was point prevalence abstinence (7-day recall, biochemically validated using expired carbon monoxide) at 12 weeks. The 52-week follow-up results are reported here. RESULTS: Participants (mean age 52 ± 13 years; 47% female) smoked a mean of 21 ± 11 cigarettes/day at baseline. Compared to individual counseling alone, participants randomized to nicotine e-cigarettes plus counseling had higher rates of point prevalence (23.6% vs 9.9%; difference: 13.7%; 95% CI: 4.6%-22.8%) and continuous abstinence (3.1% vs 0.0%; difference: 3.1%; 95% CI: 0.1%-6.2%) and greater reductions in the number of cigarettes smoked (-9.5 ± 10.5 vs -5.6 ± 9.5; difference: -3.9; 95% CI: -6.5 to -1.4) at 52 weeks. Benefits were also observed among participants randomized to non-nicotine e-cigarettes plus counseling vs counseling alone. No differences in abstinence or reduction were found between nicotine and non-nicotine e-cigarettes. CONCLUSIONS: Compared to individual counseling alone, short-term use of standardized nicotine and non-nicotine e-cigarettes plus counseling is efficacious at increasing smoking abstinence at 52 weeks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".