Opioid agonist treatment outcomes among individuals with a history of nonfatal overdose: Findings from a pragmatic, pan‐Canadian, randomized control trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: History of nonfatal overdose (NFO) is common among people who use opioids, but little is known about opioid agonist treatment (OAT) outcomes for this high-risk subpopulation. The objective of this study was to investigate the relative effectiveness of buprenorphine/naloxone and methadone on retention and suppression of opioid use among individuals with opioid use disorder (OUD) and history of NFO. METHODS: Secondary analysis of a pan-Canadian pragmatic trial comparing flexible take-home buprenorphine/naloxone and supervised methadone for people with OUD and history of NFO. Logistic regression was used to examine the impact of OAT on retention in the assigned or in any OAT at 24 weeks and analysis of covariance was used to examine the mean difference in opioid use between treatment arms. RESULTS: Of the 272 randomized participants, 155 (57%) reported at least one NFO at baseline. Retention rates in the assigned treatment were 17.7% in the buprenorphine/naloxone group and 18.4% in the methadone group (adjusted odds ratio [AOR] = 0.54, 95% CI: 0.17-1.54). Rates of retention in any OAT were 28% and 20% in the buprenorphine/naloxone and methadone arms, respectively (AOR = 1.55, 95% CI: 0.65-3.78). There was an 11.9% adjusted mean difference in opioid-free urine drug tests, favoring the buprenorphine/naloxone arm (95% CI: 3.5-20.3; p = .0057). CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Among adults with OUD and a history of overdose, overall retention rates were low but improved when retention in any treatment was considered. These findings highlight the importance of flexibility and patient-centered care to improve retention and other treatment outcomes in this population.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".