Nasal Opioid Agonist Treatment in Patients with Severe Opioid Dependence: A Case Series
Bibliographic record
Abstract
INTRODUCTION: Opioid agonist treatment (OAT) is the first-line treatment for opioid dependence. Currently available OAT options comprise oral (methadone and morphine) and sublingual (buprenorphine) routes of administration. In Switzerland and some other countries, severely opioid-dependent individuals with insufficient response to oral or sublingual OAT are offered heroin-assisted treatment (HAT), which involves the provision of injected or oral medical heroin (diacetylmorphine [DAM]). However, many patients on treatment with injectable DAM (i-HAT) suffer from injection-related problems such as deteriorated vein status, ulcerations, endocarditis, and abscesses. Other patients who do not respond to oral OAT do not inject but snort opioids, and are not eligible for i-HAT. For this population, there is no other short-acting OAT with rapid onset of action available unless they switch to injecting, which is associated with higher risks. Nasal DAM (n-HAT) could be an alternative treatment option suitable for both populations of patients. METHODS: We present a case series of 3 patients on i-HAT who successfully switched to n-HAT. RESULTS/CONCLUSIONS: This is the first description of the clinical use of the nasal route of administration for HAT. n-HAT may constitute an important risk-reduced rapid-onset alternative to i-HAT. In particular, it may be suited for patients with injection-related complications, or noninjecting opioid-dependent patients failing to respond to oral OAT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".