Reducing rates of preventable HIV/AIDS-associated mortality among people living with HIV who inject drugs
Bibliographic record
Abstract
PURPOSE OF REVIEW: The modern antiretroviral therapy (ART) era has seen substantial reductions in mortality among people living with HIV. However, HIV-positive people who inject drugs (PWIDs) continue to experience high rates of suboptimal HIV-related outcomes. We review recent findings regarding factors contributing to premature and preventable mortality among HIV-positive PWID, and describe the promise of interventions to improve survival in this group. RECENT FINDINGS: The current leading causes of death among HIV-positive PWID are HIV/AIDS-related causes, overdose, and liver-related causes, including infection with hepatitis C virus. Elevated mortality levels in this population are driven by social-structural barriers to ART access and adherence, particularly criminalization and stigmatization of drug use. In contexts where opioid substitution therapy and ART adherence support programs are widely accessible, evidence highlights comparable levels of survival among HIV-positive PWID and people living with HIV who do not inject drugs. SUMMARY: The life-saving benefits of ART can be realized among HIV-positive PWID when it is paired with strategies that address barriers to evidence-based medical care. Joint administration of ART and opioid substitution therapy, as well as repeal of punitive laws that criminalize drug users, are urgently needed to reduce HIV and injection-related mortality among PWID.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".