Correction to: Opioid Agonist Maintenance Treatment Outcomes—The OPTIMUS International Consensus Towards Evidence-Based and Patient-Centred Care, an Interim Report
Bibliographic record
Abstract
Non-medical opioid use is a major public health concern causing high mortality. While opioid agonist maintenance treatment (OMT) is a key life-saving intervention, there is (a) no international consensus on opioid treatment outcomes and (b) few opioid treatment outcome studies include key (public) health outcomes, such as overdose or HIV/hepatitis C. We report the rationale and study protocol for, and preliminary results of, an on-going international OMT outcomes consensus study that aims to address this double gap (n = 110 collaborating experts from 32 countries, plus a n = 477 Delphi evaluation panel from 26 of those countries: 58% male, 41% female; 47% OMT patients, 53% OMT professionals). We present a first draft of a patient interview guide (including a ‘clinical form’) to monitor OMT outcomes in six domains. The form appears to be well accepted and feasible in early testing. Through this, we aim to enhance the quality of and access to OMT and improve the survival, health, and quality of life of people who use opioids, while promoting non-stigmatising patient-physician relationships.
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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 | 0.012 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.090 | 0.042 |
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".