Evaluating Nicotine Abstinence, Smoking Cessation, Reduction and its Relapsed Among Electronic Cigarettes Single and Dual Malaysian Users: A One Year Observational Study
Bibliographic record
Abstract
PURPOSE: Evidence for the complete nicotine cessation is inadequate among electronic cigarettes (ECs) single users (SUs, use only ECs), and dual users (DUs, use both ECs and conventional cigarettes (CCs). The primary aim of this study was to evaluate the nicotine cessation among SUs and DUs who used ECs over one year. METHODS: We observed 70 SUs and 148 DUs for 52 weeks and tested their exhaled carbon monoxide and saliva cotinine to confirm their complete nicotine cessation status through cotinine in saliva. Safety issues were to be identified through self-report. Smoking cessation, CCs reduction of ≥ 50%, and relapsed to CCs smoking and safety issues were also documented. RESULTS: The nicotine cessation rate was higher in SUs then DUs (15.9% vs. 6.8%; P = 0.048; 95% CI (2.328-0.902). A similar result for smoking cessation (34.8% SUs vs. 17.1% DUs; P = 0.005; 95% CI: 2.031-0.787), whereas CCs ≥ 50% reduction was 23.3% DUs vs 21.7% SUs (P = 0.863; 95% CI :1.020-0.964). Relapse to CC smoking was 47.3% in DUs versus 30.4% in SUs (P = 0.026; 95% CI: 1.555-0.757). The adverse effects reported were coughing and breathing problems, whereas craving smoking was documented as a major withdrawal symptom. Smoking-related diseases were also identified, five in DUs and two in SUs, during the one-year study period. CONCLUSIONS: Study showed SUs achieved higher complete nicotine and smoking cessation rates as compared to DUs. However, the rates of reduced CC use were not different between both the groups. No serious adverse effects related to the sole use of ECs were detected. However, the safety of the sole use of ECs in absolute terms needs to be further validated in different populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".