HIV Treatment Initiation and Retention Among Individuals Initiated on Injectable Opioid Agonist Therapy for Severe Opioid Use Disorder: A Case Series
Bibliographic record
Abstract
OBJECTIVES: Injectable opioid agonist therapy (iOAT) has previously been demonstrated to be an effective treatment option for individuals with a severe opioid use disorder (OUD) who have been unsuccessful on first line therapy (eg, buprenorphine/naloxone or methadone). Many individuals with severe OUD may also have HIV infection. Despite this, no literature currently exists examining the relationship between antiretroviral therapy (ART) initiation and adherence following iOAT initiation in the outpatient setting. METHODS: Retrospective case series (n = 3) of HIV-infected individuals with a severe OUD who were refractory to oral opioid agonist treatment and were started on iOAT in a community setting in Vancouver, Canada. Outcomes of interest included: (1) iOAT induction and maintenance dosing schedules; (2) ART adherence demonstrated by change in HIV viral load. RESULTS: All 3 patients initiated and successfully reached iOAT maintenance doses with significant reduction in illicit opioid use. Stable iOAT was associated with increased ART initiation and adherence, and decreased HIV viral loads. Conversely, poor retention or discontinuation of iOAT was associated with reduced adherence to ART and in 1 patient, increased HIV viral loads. CONCLUSIONS: The individual cases presented suggest that among individuals with severe OUD and HIV infection, iOAT may improve HIV treatment uptake and retention in care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".