Pre-exposure prophylaxis for sexually-acquired HIV risk management: a review
Bibliographic record
Abstract
Despite significant efforts, the rate of new HIV infections worldwide remains unacceptably high, highlighting the need for new HIV prevention strategies. HIV pre-exposure prophylaxis (PrEP) is a new approach that involves the ongoing use of antiretroviral medications by HIV-negative individuals to reduce the risk of HIV infection. The use of daily tenofovir/emtricitabine as oral PrEP was found to be effective in multiple placebo-controlled clinical trials and approved by the United States Food and Drug Administration. In addition, the Centers for Disease Control and Prevention in the United States and the World Health Organization have both released guidelines recommending the offer of oral PrEP to high-risk populations. The scale-up of PrEP is underway, but several implementation questions remain unanswered. Demonstration projects and open-label extensions of placebo-controlled trials are ongoing and hope to contribute to our understanding of PrEP use and delivery outside the randomized controlled trial setting. Evidence is beginning to emerge from these open-label studies and will be critical for guiding PrEP scale-up. Outside of such studies, PrEP uptake has been slow and several client- and provider-related barriers are limiting uptake. Maximizing the public health impact of PrEP will require rollout to be combined with interventions to promote uptake, support adherence, and prevent increases in risk behavior. Additional PrEP strategies are currently under investigation in placebo-controlled clinical trials and may be available in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.006 | 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".