Nasal administration of diacetylmorphine improved the adherence in a patient receiving heroin-assisted treatment
Bibliographic record
Abstract
BACKGROUND: Traditional heroin-assisted treatment in Switzerland consists of oral and injectable diacetylmorphine (pharmaceutical heroin) administration. To date, no suitable treatment option is available for patients who crave rapid onset ("rush") but are either unable to inject or primarily sniff or inhale illicit heroin. We present a patient who successfully switched to intranasal heroin-assisted treatment following several unsuccessful treatment attempts. CASE PRESENTATION: A 29-year-old male with severe opioid use disorder, injection substance use, and concomitant cocaine use, previously prescribed slow-release oral morphine, was started on intravenous diacetylmorphine. Due to complications and harms associated with intravenous injections, nasal diacetylmorphine was prescribed. With this novel route of administration, the patient who had previously been unable to adhere to other OAT options remained in treatment. Health outcomes improved by reduction of injection-related harms, increased adherence to the heroin-assisted treatment regimen, and increased collaboration with the therapeutic staff. CONCLUSIONS: Nasal heroin-assisted treatment can be a feasible therapeutic option for individuals with severe opioid use disorder who crave the fast onset of effect of diacetylmorphine but are unable to inject intravenously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".