Impact of Depressive Symptom Severity on Buprenorphine/Naloxone and Methadone Outcomes in People With Prescription-Type Opioid Use Disorder: Results From a Pragmatic Randomized Controlled Trial
Bibliographic record
Abstract
Objective To evaluate the impact of depressive symptom severity on opioid use and treatment retention in individuals with prescription-type opioid use disorder (POUD).Method We analyzed data from a multi-centric, pragmatic, open-label, randomized controlled trial comparing buprenorphine/naloxone to methadone models of care in 272 individuals with POUD. Opioid use was self-reported every two weeks for 24 weeks using the Timeline Followback. Depressive symptom severity was self-reported with the Beck Depression Inventory at baseline, week 12 and week 24.Results Baseline depressive symptom severity was not associated with opioid use nor treatment retention. At week 12, moderate depressive symptoms were associated with greater opioid use while mild to severe depressive symptoms were associated with lowered treatment retention. At week 24, moderate depressive symptoms were associated with greater opioid use.Conclusions Ongoing depressive symptoms lead to poorer outcomes in POUD. Clinicians are encouraged to use integrative approaches to optimize treatment outcomes. This study was registered in ClinicalTrials.gov (NCT03033732) on January 27th, 2017, prior to participants enrollment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".