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Record W4281746757 · doi:10.1186/s12954-022-00644-2

Nasal administration of diacetylmorphine improved the adherence in a patient receiving heroin-assisted treatment

2022· article· en· W4281746757 on OpenAlexaff
Maximilian Meyer, Jean N. Westenberg, Johannes Strasser, Kenneth M. Dürsteler, Undine E. Lang, Michael Krausz, Marc Vogel

Bibliographic record

VenueHarm Reduction Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHealth psychologyHeroinMedicineAdministration (probate law)Heroin dependencePublic healthPsychologyPsychiatryNursingDrugPolitical science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.339
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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