Transitioning a patient from injectable opioid agonist therapy to sublingual buprenorphine/naloxone for the treatment of opioid use disorder using a microdosing approach
Bibliographic record
Abstract
In the wake of North America's opioid crisis, access to evidence-based treatment for opioid use disorder (OUD) is of critical importance. While buprenorphine/naloxone and methadone are currently indicated as first-line medications for the treatment of OUD, there are a proportion of individuals who do not benefit from these therapies. Recent Canadian guidelines suggest the use of alternate therapies, including slow-release oral morphine or injectable opioid agonist therapy (iOAT) for individuals unsuccessful with either methadone or buprenorphine/naloxone. While the guidelines highlight the need to intensify OUD treatment as disease severity increases, equally important is the consideration for deintensification of treatment (eg, from iOAT to an oral opioid agonist treatment (OAT) option) following successful stabilisation. Literature addressing how best to accomplish this, however, is currently lacking. Accordingly, the case presented here describes a patient that successfully transitions from iOAT to oral buprenorphine/naloxone using a novel induction approach termed microdosing.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".