The impact of Thai multidisciplinary smoking cessation program on clinical outcomes: A multicentre prospective observational study
Bibliographic record
Abstract
Introduction: Tobacco use is the leading preventable cause of morbidity and mortality worldwide. Since 2010, Thailand has implemented a multidisciplinary smoking cessation clinic, which provides smoking cessation services, but the effectiveness of the clinics was not formally evaluated. This study was conducted to assess the real-world effectiveness of this multidisciplinary smoking cessation program. Methods: We conducted a prospective, multicentre, observational study on Thai participants aged 13 years and older in 24 smoking cessation clinics across Thailand's 13 health regions. Each clinic offered smoking cessation interventions according to the well-established 5As model for smoking cessation (Ask, Advise, Assess, Assist, and Arrange). Outcomes of interest were continuous abstinence rates (CAR) at 3 and 6 months. Biochemical confirmation and self-reporting were used to assess the outcomes. Descriptive statistics (mean, SD, median, IQR, and percentage) were used to analyze the smoking cessation outcomes in both intention-to-treat and per-protocol analysis approaches. Results: Smokers receiving services from the Thai multidisciplinary smoking cessation clinics had CAR of 17.49 and 8.33% at 3 and 6 months, respectively. For those with cardiovascular disease (CVD) or cerebrovascular disease, CAR was found to be 26.36% at 3 months and 13.81% at 6 months. While participants with chronic obstructive pulmonary disease (COPD) had CAR ranging from 32.69% at 3 months to 17.31% at 6 months. Conclusion: The multidisciplinary team smoking cessation clinic was effective in assisting smokers in quitting smoking. The effectiveness of the clinic was more pronounced for smokers with CVD, cerebrovascular disease, or COPD. Findings from this study support a decision to include multidisciplinary smoking cessation clinics in the universal health care benefits package.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".