Impact of opioid agonists on mental health in substitution treatment for opioid use disorder: A systematic review and Bayesian network meta-analysis of randomized clinical trials
Bibliographic record
Abstract
Abstract Objective There is a dearth of high-quality systematic evidence on the impact of opioid substitution medications on mental health. We compared mental health outcomes between opioid medications and placebo/waitlist, and between different opioids. Methods This systematic review and meta-analysis of randomized clinical trials (RCTs) was pre-registered at PROSPERO (CRD42018109375). Embase, MEDLINE, PsychInfo, CINAHL Complete, and Web of Science Core Collection were searched from inception to May 2020. RCTs were included if they compared opioid agonists with each other or with a placebo/waitlist in substitution treatment of patients with opioid use disorder, and reported at least one mental health outcome on a span of more than 1-month post baseline. Studies with psychiatric care, adjunct psychotropic medications, or unbalanced psychosocial services were excluded. Primary outcomes were comparison of depressive symptoms and overall mental health between opioids and placebo/waitlist. Random effects model was used for all the meta-analysis. Results Nineteen studies were included in the narrative synthesis and 15 in the quantitative synthesis. Hydromorphone, diacetylmorphine (DAM), methadone, slow-release oral morphine, buprenorphine, and placebo/waitlist were among the included interventions. Based on network meta-analysis for primary outcomes, buprenorphine (SMD (CI95%)= −0.61 (−1.20, −0.11)), DAM (−1.40 (−2.70, −0.23)), and methadone (−1.20 (−2.30, −0.11)) were superior to waitlist/placebo on overall mental health. Further direct pairwise meta-analysis indicated that overall mental health improved more in DAM compared to methadone (−0.23 (−0.34, −0.13)). Conclusions It appears that opioid medications improve mental health independent of psychosocial services. Potential contribution of other factors needs to be further investigated.
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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 | 0.053 | 0.103 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.054 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".