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Record W2918591540 · doi:10.1080/08897077.2019.1576086

Successful Treatment with Slow-Release Oral Morphine following Afentanyl-Related Overdose: A Case Report

2019· article· en· W2918591540 on OpenAlexaff
Gerrit Prinsloo, Keith Ahamad, M. Eugenia Socías

Bibliographic record

VenueSubstance Abuse · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsMorphineMedicineDrug overdoseExtended releaseOpioid overdoseOpioidAnesthesiaIntensive care medicinePharmacology(+)-NaloxoneMedical emergencyPoison controlInternal medicine

Abstract

fetched live from OpenAlex

Background Overdose deaths as a result of untreated opioid use disorder (OUD) pose a major public health concern across North America. Although slow-release oral morphine (SROM) is increasingly used as an alternative option for the treatment of OUD, research on its efficacy among individuals exposed to illicit fentanyl or those with previous unsuccessful attempts with other opioid agonist therapies (OATs) is limited and controversial. Case We present a case of a 48-year-old male with severe OUD seeking treatment following a near-fatal fentanyl overdose. His previous treatment attempts with methadone and buprenorphine/naloxone-based OAT had been unsuccessful. As per local guidelines, he was started on SROM with subsequent cessation of opioid cravings and illicit drug use. Discussion This case report describes a patient entering early remission for OUD when treated with SROM following unsuccessful past treatment attempts on first-line oral medications. Future studies should seek to evaluate SROM-based OAT as a potential second-line treatment alternative for OUD.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.033
GPT teacher head0.356
Teacher spread0.322 · 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.

Study designCase report
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

Citations2
Published2019
Admission routes1
Has abstractyes

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