Sexually transmitted and blood‐borne infection risk reduction with methadone and buprenorphine/naloxone among people with prescription‐type opioid use disorder: Findings from a Canadian pragmatic randomized trial
Bibliographic record
Abstract
INTRODUCTION: People who use drugs are disproportionally affected by sexually transmitted and blood-borne infections (STBBIs). While the benefits of methadone in reducing injecting-risk behaviours are well documented, less is known on its impacts on sexual-related risks, as well as its comparative effectiveness to buprenorphine/naloxone, particularly in the context of highly potent opioids. The aim of this study was to estimate the relative effects of buprenorphine/naloxone and methadone on injecting and STBBI risks among people with prescription-type opioid use disorder (POUD). METHODS: Secondary analysis of a pan-Canadian pragmatic 24-week randomized clinical trial comparing methadone and buprenorphine/naloxone models of care among 272 people with POUD (including licit or illicit opioid analgesics, fentanyl). The Risk Behaviour Survey was used to collect injecting and sexual risks at baseline, and weeks 12 and 24. RESULTS: In total, 210 participants initiated treatment (103 buprenorphine/naloxone and 107 methadone). At baseline, 113/205 (55.1%) participants reported recently injecting drugs, 37/209 (17.7%) unsafe injection practices and 67/162 (41.4%) high-risk sex. Both methadone and buprenorphine/naloxone were associated with reductions in the prevalence of injection drug use and high-risk sex at weeks 12 and 24 with no interactions between treatment arm and time. CONCLUSION: Methadone and buprenorphine/naloxone were similarly effective in reducing injecting and sexual risk behaviours among people with POUD. CLINICAL TRIALS REGISTRATION: clinicaltrials.gov NCT03033732.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".