Antiretroviral Therapy: A Promising HIV Prevention Strategy?
Bibliographic record
Abstract
The use of antiretroviral therapy (ART) has been associated with significant improvement in morbidity and survival of persons living with HIV. In addition, recently, there has also been intense interest in the potential impact of ART on HIV transmission and consequently on the trajectory of the HIV epidemic globally. Evidence from mathematical modeling analyses and observational and ecological studies supports the potential for ART as prevention. However, definitive data from clinical trials are awaited. In the United States, the feasibility and potential of using ART as a prevention strategy presents particular challenges: the large number of individuals with undiagnosed HIV; the predominance of disenfranchised individuals affected by the epidemic; evidence of delay in engagement in HIV care after diagnosis with attendant late initiation of ART; and difficulties with consistent long-term adherence to ART and concerns regarding long-term risk-behavior change. Thus, for this novel effort to succeed, a multidimensional approach is necessary that must include policy changes, social mobilization, and improved access to clinical and supportive services for persons living with HIV, with a particular focus on the unique needs of at-risk populations, combined with engagement of all cadres of health care providers and community constituencies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".