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Record W2993777813 · doi:10.1111/dar.13020

Initiation of injectable opioid agonist treatment in hospital: A case report

2019· article· en· W2993777813 on OpenAlexaff
Matthew McAdam, Rupinder Brar, Samantha Young

Bibliographic record

VenueDrug and Alcohol Review · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of British ColumbiaDalhousie University
FundersNational Institute on Drug Abuse
KeywordsHydromorphoneBuprenorphineMedicineMethadoneOpioidAgonist(+)-NaloxoneOpioid use disorderAddictionHeroinAnesthesiaFentanylNarcotic antagonistsDrugPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Uncontrolled opioid withdrawal and pain often drive inpatients with opioid use disorder to leave hospital against medical advice, resulting in suboptimal medical and addiction treatment. When oral opioid agonist treatments such as methadone and buprenorphine/naloxone fail for management of craving and withdrawal, injectable opioid agonist treatment may serve to retain patients in care and link them to addiction services. We describe the case of a 47-year-old man with a severe, active opioid use disorder and daily use of illicitly manufactured fentanyl, who was re-admitted to hospital for post-operative management after leaving against medical advice due to uncontrolled opioid withdrawal. Intravenous hydromorphone was used to retain him in care, allowing for completion of his antibiotics and enrolment in ongoing community injectable opioid agonist treatment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2019
Admission routes1
Has abstractyes

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