A pilot, open-label, 8-week study evaluating desvenlafaxine for treatment of major depression in methadone-maintained individuals with opioid use disorder
Bibliographic record
Abstract
Depression is one of the most prevalent psychiatric disorders among opioid-dependent individuals. Clinical trials testing selective serotonin reuptake inhibitors among depressed patients on methadone maintenance therapy (MMT) failed to show efficacy, whereas those on tricyclic antidepressants produced mixed results with potential for cardiotoxicity. Desvenlafaxine (DESV) is a SNRI with minimal cardiotoxicity and drug interactions. This study sought to assess feasibility and tolerability of using DESV in depressed patients on MMT. A total of 18 depressed individuals on MMT received DESV (50-100 mg/day) for 8 weeks. Participants were assessed for the following: (a) Safety of DESV using Systematic Assessment for Treatment Emergent Events-GI, ECG [corrected Q-T (QTc) interval measurement] and methadone serum levels; (b) depressive symptoms using Montgomery-Äsberg Depression Rating Scale (MADRS); and (c) other outcomes including anxiety, suicidality, craving, substance use, quality of life, and other depression scales. Registration number on ClinicalTrials.gov is NCT02200406. Among participants who completed the study, MADRS scores significantly decreased at week 8 compared with baseline. Responders and remitters on MADRS at week 8 were 61 and 50%, respectively. There was no significant change in [corrected Q-T (QTc) interval measurement] between baseline and week 4. DESV was well tolerated and associated with improvement of depressive symptoms. DESV may be a promising contender to treat depression in individuals on MMT and deserves further exploration in a randomized double-blinded clinical trial.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".