Impact of the Ottawa model on opiate screening and smoking cessation in methadone-treated patients with opioid use disorder: A retrospective cohort analysis
Bibliographic record
Abstract
INTRODUCTION: Approximately 60 million individuals worldwide used opioids in 2021, constituting 1.2% of the global adult population. This study aimed to evaluate the effectiveness of integrated treatment strategies for opioid use disorder and nicotine use disorder by assessing the impact of smoking cessation within a methadone treatment framework. METHODS: In a retrospective cohort study, 53 methadone maintenance patients were divided into 16 treatment-seeking smokers (TSS) and 37 treatment-rejecting smokers (TRS) based on their participation in the Ottawa model for smoking cessation plus 16 weeks of varenicline treatment. Both groups received standard methadone treatment for 68 weeks. TSS were followed up for 44 weeks to assess smoking cessation outcomes, while TRS had none due to their lack of participation in smoking cessation treatment. RESULTS: The median age of the TSS group was 48 years, while that of the TRS group was 45.5 years. Males comprised 75% of TSS and 94.6% of the TRS. TSS exhibited an 83% decrease in positive opioid screen results compared to TRS (p=0.023). In TSS, peak smoking cessation success was observed at week 20, with 57% of participants maintaining carbon monoxide levels <5 ppm. CONCLUSIONS: The significant reduction in positive opioid screens and the high smoking cessation rate in the TSS group highlight the efficacy of combined treatment methods. This study underscores the advantages of integrating smoking cessation with methadone maintenance treatment, indicating that comprehensive approaches can substantially improve treatment outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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".