Evolving uses of oral reverse transcriptase inhibitors in the HIV-1 epidemic: from treatment to prevention
Bibliographic record
Abstract
The HIV epidemic continues unabated, with no highly effective vaccine and no cure. Each new infection has significant economic, social and human costs and prevention efforts are now as great a priority as global antiretroviral therapy (ART) scale up. Reverse transcriptase inhibitors, the first licensed class of ART, have been at the forefront of treatment and prevention of mother to child transmission over the past two decades. Now, their use in adult prevention is being extensively investigated. We describe two approaches: treatment as prevention (TasP) - the use of combination ART (2NRTI and 1NNRTI) following HIV diagnosis to limit transmission and pre-exposure prophylaxis (PrEP) -the use of single or dual oral agents prior to sexual exposure. Prevention of mother-to-child transmission using NRTI has been highly successful, though does not involve sustained use of NRTI to limit transmission. Despite theoretical and preliminary support for TasP and PrEP, data thus far indicate that adherence, retention in care and late diagnosis are the major barriers to their successful, sustained implementation. Future advances in drug technologies will be needed to overcome the issue of drug adherence, through development of drugs that involve both less frequent dosing as well as reduced toxicity, possibly through specific targeting of infected cells.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".