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Record W2460625433 · doi:10.1136/bcr-2016-215557

Treatment of opioid use disorder in an innovative community-based setting after multiple treatment attempts in a woman with untreated HIV

2016· article· en· W2460625433 on OpenAlexaff
Pauline Voon, Ronald Joe, Christopher Fairgrieve, Keith Ahamad

Bibliographic record

VenueBMJ Case Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsVancouver Coastal HealthAIDS VancouverUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsHarm reductionMedicineMethadoneAbstinenceOpioid use disorderPsychiatryPharmacotherapyPsychological interventionHeroinOpiate Substitution TreatmentIntensive care medicineDosingMethadone maintenanceDirectly Observed TherapySubstance abuseOpioidHuman immunodeficiency virus (HIV)DrugInternal medicineFamily medicineBuprenorphine

Abstract

fetched live from OpenAlex

Opioid use disorder is associated with significant health and social harms. Various evidence-based interventions have proven successful in mitigating these harms, including harm reduction strategies and pharmacological treatment such as methadone. We present a case of a 35-year-old HIV-positive woman who was off antiretroviral therapy due to untreated opioid use disorder, and had a history of frequently self-discharging from hospital against medical advice. During the most recent hospital admission, the patient was transferred to an innovative community-based clinical support residence that supported harm reduction. Initially, she received methadone to only manage the withdrawal symptoms rather than for long-term maintenance therapy. However, with gradual dose increases to treat cravings and withdrawal, she ultimately discontinued all drug use and reinitiated antiretroviral therapy. This case highlights that patients whose goal is not abstinence can be successfully treated for acute medical illnesses and comorbid substance use disorders using harm reduction approaches, including appropriate dosing of pharmacotherapy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.356
Teacher spread0.301 · 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 designObservational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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