The effect of craving on retention and treatment switching under buprenorphine-naloxone and methadone models of care for non-heroin opioid use disorder: Exploratory analyses from a pragmatic, randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Though opioid agonist therapies are the mainstay of treatment for opioid use disorder, treatment retention remains suboptimal. Improved prediction of who will remain in treatment could lead to improved treatment outcomes. Whether craving predicts reduced retention in treatment remains debated. We performed analyses to determine whether craving predicted treatment attrition or treatment switching in people with non-heroin opioid use disorder initiating opioid agonist therapy. METHODS: Our data came from the OPTIMA trial - a pan-Canadian, pragmatic, open-label, randomized controlled trial that compared a flexible, early take-home buprenorphine/naloxone model of care (n = 137) to standard treatment with methadone (n = 132) for non-heroin opioid use disorder over a period of 24 weeks. We performed Cox proportional hazards regression to conduct survival analyses of time (days) to treatment attrition, and time to switch to another treatment, with craving as a time-varying covariate, controlling for assigned treatment group, lifetime history of heroin use and province. Craving was measured at baseline, week 2, 6, 10, 14, 18, 22 using the Brief Substance Craving Scale. RESULTS: We found that craving predicted both treatment drop out and treatment switching. A 1-point increase in craving was associated with a 15.3 % increase of risk of dropping out of the study (HR = 1.153, 95 % CI = 1.065 to 1.248, p < 0.001) and with a 11.5 % increase of risk of switching treatment (HR = 1.115, 95 % CI = 1.016 to 1.225, p = 0.022). CONCLUSIONS: Craving predicted both treatment attrition and treatment switching in people receiving buprenorphine/naloxone or methadone models of care for non-heroin opioid use disorder. These findings highlight the importance of targeting and better addressing craving during treatment with opioid agonist therapies.
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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) | 0.007 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".