Relative risks of adverse effects across different opioid agonist treatments—A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Opioid agonist treatment (OAT) is established for opioid use disorder, but limited data on adverse effects exist. We aimed to review relative risks of adverse effects across opioid agonists. METHODS: Systematic review with pair-wise meta-analysis of randomized clinical trials. A search in Embase, Medline, PsycInfo, CENTRAL and the Web of Science Core Collection was performed from inception to 11 April 2024 (PROSPERO: CRD42022322722). Two reviewers independently extracted data and used the Cochrane Risk of Bias Assessment Tool. Certainty of evidence was assessed using GRADE (Grading of Recommendations Assessment, Development and Evaluation). Primary outcomes were constipation, sedation, pruritus, sweating, nausea and vomiting, headache and any non-headache pain. RESULTS: We identified 25 eligible trials, including 4892 participants. Reported agonists were methadone, levomethadone, methadyl acetate, buprenorphine, buprenorphine/naloxone, slow-release oral morphine (SROM), diacetylmorphine, hydromorphone and opium tincture. Buprenorphine (all formulations combined) was associated with a lower risk of sedation than methadone [risk ratio (RR) = 0.68 (95% confidence interval = 0.56-0.82)]; 1558 participants, 9 studies]. Methadone had a lower risk of sedation compared with SROM [RR = 0.63 (0.58-0.69); 356 participants, 2 studies] and a lower risk of nausea than methadyl acetate [RR = 0.56 (0.37-0.85); 913 participants, 3 studies]. There was high overall risk of bias in 92% of included trials due to limited and non-systematic outcome assessment. Certainty of evidence was low or very low for all but one comparison with moderate certainty. CONCLUSIONS: There is currently insufficient data to determine whether the rates of adverse effects differ across opioid agonist treatments for most outcomes, with several exceptions. Moreover, the certainty of evidence is currently low or very low due to a lack of rigorous outcome assessment.
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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 | 0.039 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.024 | 0.067 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".