Slow release oral morphine versus methadone for the treatment of opioid use disorder
Bibliographic record
Abstract
Objective To assess the efficacy of slow release oral morphine (SROM) as a treatment for opioid use disorder (OUD). Design Systematic review and meta-analysis of randomised controlled trials (RCTs). Data sources Three electronic databases were searched through 1 May 2018: the Cochrane Central Register of Controlled Trials, MEDLINE and EMBASE. We also searched the following electronic registers for ongoing trials: ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, Current Controlled Trials and the EU Clinical Trials Register. Eligibility criteria for selecting studies We included RCTs of all durations, assessing the effect of SROM on measures of treatment retention, heroin use and craving in adults who met the diagnostic criteria for OUD. Data extraction and synthesis Two independent reviewers extracted data and assessed risk of bias. Data were pooled using the random-effects model and expressed as risk ratios (RRs) or mean differences with 95% CIs. Heterogeneity was assessed (χ 2 statistic) and quantified (I 2 statistic) and a sensitivity analysis was undertaken to assess the impact of particular high-risk trials. Results Among 1315 records screened and four studies reviewed, four unique randomised trials met the inclusion criteria (n=471), and compared SROM with methadone. In the meta-analysis, we observed no significant differences between SROM and methadone in improving treatment retention ( RR=0.98; 95%CI: 0.94 to 1.02, p=0.34) and heroin use (RR=0.96; 95% CI: 0.61 to 1.52, p=0.86). Craving data was not amenable to meta-analysis. Available data implied no differences in adverse events, heroin, cocaine or benzodiazepine use. Conclusions Meta-analysis of existing randomised trials suggests SROM may be generally equal to methadone in retaining patients in treatment and reducing heroin use while potentially resulting in less craving. The methodological quality of the included RCTs was low-to-moderate.
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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 | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".