Nasal Opioid Agonist Treatment in Patients with Severe Opioid Dependence: A Case Series
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it