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Record W2964669656 · doi:10.1136/bcr-2019-229456

Supervised injectable opioid agonist therapy in a supported housing setting for the treatment of severe opioid use disorder

2019· article· en· W2964669656 on OpenAlexaff
Rupinder Brar, Christy Sutherland, Seonaid Nolan

Bibliographic record

VenueBMJ Case Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsOpioid use disorderBuprenorphineMethadoneOpioidMedicineAgonistOpiate Substitution TreatmentPsychiatryIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Currently, North America is facing a national opioid overdose crisis. Opioid use disorder (OUD) is a chronic, relapsing condition requiring varying intensities of treatment. Injectable opioid agonist therapy (iOAT) is an effective, high-intensity treatment option for people who are unsuccessful on conventional oral opioid agonist treatments (eg, methadone, buprenorphine). This case highlights the effectiveness of the provision of iOAT in a low-barrier setting. More specifically, we discuss a patient with severe OUD and untreated HIV infection, who was disengaged from medical care and, consequently, was prescribed iOAT in a supportive housing setting.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.027
GPT teacher head0.309
Teacher spread0.283 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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