Global opioid agonist treatment: a review of clinical practices by country
Bibliographic record
Abstract
AIMS: We assessed how opioid agonist treatment (OAT) for opioid use disorder (OUD), specifically methadone and buprenorphine, including buprenorphine-naloxone, is delivered in routine clinical practice, with a focus on factors that affect access to and delivery of these services. The aims of this review were to summarize eligibility criteria for entry to OAT, doses in routine clinical practice, access to and eligibility for unsupervised dosing and urine drug screening practices in OAT programs globally. METHODS: We completed searches of PubMed, Embase, and grey literature databases for cross-sectional or observational cohort studies of OAT using either methadone or buprenorphine. Dose data extracted from eligible studies were compared with guidelines provided by WHO. RESULTS: We found 140 reports from 41 countries that contained data for at least one of the relevant indicators. A diagnosis of opioid dependence or opioid use disorder was the most common eligibility requirement for OAT (13 or 17 countries). Reported mean or median doses for methadone ranged from 16-131 mg whereas range for buprenorphine was 2.5-19 mg. Access to unsupervised dosing under some conditions was reported in 18 of 27 countries. Frequency of regular urine drug screenings (UDS) ranged from several times a week to eight times per year (methadone) or as clinically indicated. CONCLUSIONS: Opioid agonist treatment practices, including doses prescribed, vary greatly both within and across countries. Of particular concern is the persistence of lower dose prescribing practices, in which patients may be prescribed doses below those proven to yield significant clinical benefits.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".