E-cigarette use in young Swiss men: is vaping an effective way of reducing or quitting smoking?
Bibliographic record
Abstract
QUESTION UNDER STUDY: To test longitudinally differences in conventional cigarette use (cigarettes smoked, cessation, quit attempts) between vapers and nonvapers. METHODS: Fifteen months follow-up of a sample of 5 128 20-year-old Swiss men. The onset of conventional cigarette (CC) use among nonsmokers, and smoking cessation, quit attempts, changes in the number of CCs smoked among smokers at baseline were compared between vapers and nonvapers at follow-up, adjusted for nicotine dependence. RESULTS: Among baseline nonsmokers, vapers were more likely to start smoking at follow-up than nonvapers (odds ratio [OR] 6.02, 95% confidence interval [CI] 2.81, 12.88 for becoming occasional smokers, and OR = 12.69, 95% CI 4.00, 40.28 for becoming daily smokers). Vapers reported lower smoking cessation rates among occasional smokers at baseline (OR = 0.43 (0.19, 0.96); daily smokers: OR = 0.42 [0.15, 1.18]). Vapers compared with nonvapers were heavier CC users (62.53 vs 18.10 cigarettes per week, p <0.001) and had higher nicotine dependence levels (2.16 vs 0.75, p <0.001) at baseline. The number of CCs smoked increased between baseline and follow-up among occasional smokers (b = 6.06, 95% CI 4.44, 7.68) and decreased among daily smokers (b = -5.03, 95% CI -8.69, -1.38), but there were no differential changes between vapers and nonvapers. Vapers showed more quit attempts at follow-up compared with nonvapers for baseline occasional smokers (incidence rate ratio [IRR] 1.81, 95% CI 1.24, 2.64; daily smokers IRR 1.28, 95% CI 0.95, 1.73). CONCLUSIONS: We found no beneficial effects of vaping at follow-up for either smoking cessation or smoking reduction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".