HIV infection among persons who inject drugs
Bibliographic record
Abstract
AIDS among persons who inject drugs, first identified in December 1981, has become a global epidemic. Injecting drug use has been reported in 148 countries and HIV infection has been seen among persons who inject drugs in 61 countries. Many locations have experienced outbreaks of HIV infection among persons who inject drugs, under specific conditions that promote very rapid spread of the virus. In response to these HIV outbreaks, specific interventions for persons who inject drugs include needle/syringe exchange programs, medicated-assisted treatment (with methadone or buprenorphine) and antiretroviral therapy. Through a 'combined prevention' approach, these interventions significantly reduced new HIV infections among persons who inject drugs in several locations including New York City, Vancouver and France. The efforts effectively ended the HIV epidemic among persons who inject drugs in those locations. This review examines possible processes through which combined prevention programs may lead to ending HIV epidemics. However, notable outbreaks of HIV among persons who inject drugs have recently occurred in several countries, including in Athens, Greece; Tel-Aviv, Israel; Dublin, Ireland; as well as in Scott County, Indiana, USA. This review also considers different factors that may have led to these outbreaks. We conclude with addressing the remaining challenges for reducing HIV infection among persons who inject drugs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".