The OPTIMUS International Consensus Guidance for Monitoring User-Reported Outcomes of Opioid Maintenance Treatment: a Delphi Study
Bibliographic record
Abstract
Opioid use disorder is a major cause of drug-related harm and mortality. These can be reduced by expanded access to evidence-based and highly effective opioid agonist maintenance treatment or therapy (OMT). There is a lack of consensus on how to assess opioid use disorder treatment outcomes, and key health outcomes are often omitted. We report the results of a Delphi study to produce service user- and public health–centred international consensus guidance for OMT outcomes monitoring. An international group of 110 substance use specialists in 32 countries, including service providers, researchers and people with lived experience of OMT, produced draft guidance over multiple meetings. The guidance includes a service user-reported OMT outcomes questionnaire, based on 26 core questions, plus optional questions, in six domains (treatment, physical health, mental health, social functioning, substance use, quality of life). A Delphi panel of 757 OMT professionals and service users (46%) from 29 countries, of which 40% were female, reviewed the questionnaire over two survey rounds, supporting and improving it (round 2 mean agreement score on a 1-6 Likert scale: 5.2; 95%CI 5.1–5.3). By focusing on service user–reported and public health–centred outcomes of OMT, the OPTIMUS consensus guidance aims to facilitate the communication between service providers and service users and improve the quality of care and the survival, health and quality of life of OMT service users.
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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 | 0.338 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".