Associations of Methadone and BUP/NX Dose Titration Patterns With Retention in Treatment and Opioid Use in Individuals With Prescription-Type Opioid Use Disorder: Secondary Analysis of the OPTIMA Study
Bibliographic record
Abstract
INTRODUCTION: Methadone and buprenorphine/naloxone (BUP/NX) titration parameters (eg, range, duration, and rate) can vary during opioid use disorder (OUD) treatment. We describe methadone and BUP/NX titration patterns and their associations with treatment outcomes among individuals with a prescription-type OUD. METHODS: We used data from a 24-week open-label, multicenter randomized controlled trial, including N = 167 participants aged 18-64 years old with prescription-type OUD who received at least a first dose of treatment. Descriptive analyses of methadone and BUP/NX titration patterns were conducted, that is, range and duration from first to maximum dose, and rate (range/duration ratio). Outcomes included percentage of opioid-positive urine drug screens (UDS) and treatment retention. Adjusted linear and logistic regressions were used to study associations between titration patterns and percentage of opioid-positive UDS and treatment retention. RESULTS: Methadone doses were increased by a mean dose range of 42.4 mg over a mean duration of 42.2 days. BUP/NX doses were increased by a mean dose range of 8.4 mg over a mean duration of 28.7 days. Only methadone dose titration range (odds ratio: 1.03; 95% CI, 1.01 to 1.05) and duration (odds ratio: 1.03; 95% CI, 1.01 to 1.05) were associated with higher retention. Only methadone dose titration rate was associated with lower percentage of opioid-positive UDS at weeks 12-24 ( B : -2.77; 95% CI, -4.72 to -0.81). CONCLUSIONS: Specific parameters of methadone titration were associated with treatment outcomes and may help in personalizing treatment schedules. Sustained methadone dose titration, when indicated, may help increase retention, whereas faster dose titration for methadone may help decrease opioid use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".