Effects of Buprenorphine/Naloxone and Methadone on Depressive Symptoms in People with Prescription Opioid Use Disorder: A Pragmatic Randomised Controlled Trial
Bibliographic record
Abstract
Objective This study aimed to evaluate the effectiveness of flexible take-home dosing of buprenorphine/naloxone (BUP/NX) and methadone standard model of care in reducing depressive symptoms in people with prescription-type opioid use disorder (POUD). This trial also evaluated whether improvements in depressive symptoms were mediated by opioid use. Methods Analyzed data came from the OPTIMA study (clinicaltrials.gov identifier: NCT03033732), a pragmatic randomised controlled trial comparing flexible take-home dosing of BUP/NX and methadone standard model of care for reducing opioid use in people with POUD. A total of 272 participants were recruited in four Canadian provinces. Participants were randomised 1:1 to BUP/NX or methadone. After treatment induction, past two-week opioid use was measured using the Timeline Followback every two weeks for a total of 24 weeks. Depressive symptoms were measured with the Beck Depression Inventory at baseline, weeks 12 and 24. Results Both BUP/NX and methadone significantly reduced depressive symptoms at week 12 (aβ ± SE = −3.167 ± 1.233; P < 0.001) and week 24 (aβ ± SE = −7.280 ± 1.285; P < 0.001), with no interaction between type of treatment and time ( P = 0.284). Improvements in depressive symptoms were only partially mediated by a reduction in opioid use (proportion mediated = 36.8%; 95% confidence interval = −1.158 to −0.070; P = 0.015). Conclusions BUP/NX and methadone showed similar effectiveness in decreasing comorbid depressive symptoms in people with POUD. This effect was partially explained by a reduction in opioid use. As both treatments seem equally effective, clinicians are encouraged to tailor the selection of OAT to patients’ needs and characteristics.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".