Oral Antiretroviral Drugs as Public Health Tools for HIV Prevention: Global Implications for Adherence, Drug Resistance, and the Success of HIV Treatment Programs
Bibliographic record
Abstract
Recent data from studies on treatment as prevention (TasP) and preexposure prophylaxis (PrEP) show that antiretroviral drugs can be used in prevention, as well as in treatment. The movement from first-generation antiretroviral therapy (ART) coformulations based on thymidine analogues to second-generation ART coformulations based on tenofovir may coincide with future prevention strategies that also use tenofovir/emtricitabine, raising concerns regarding drug resistance. In published studies, failure of prophylaxis was associated with poor adherence and low plasma drug levels. Although rates of drug resistance in cases of failed prevention was low, regular human immunodeficiency virus (HIV) testing was undertaken in these clinical trials. Although legitimate concerns exist about ART adherence and drug resistance associated with PrEP and TasP in real-world settings, efforts to curb the continuing HIV epidemic through use of these novel prevention strategies should move forward because the development and approval of newer drugs reserved for prevention might take many more years. Efforts must be made to monitor ART adherence and to intervene through counseling and other means in order to optimize adherence and retention in care, whenever necessary. Finally, further research involving the generalized epidemic is needed to determine when suboptimal drug use may occur and when regular testing and monitoring of the long-term consequences of ART use may not be routine.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".