Chemoprophylaxis of HIV Infection: Moving Forward with Caution
Bibliographic record
Abstract
The study by Subbarao et al. [1] in this issue of the Journal advances the rationale for HIV chemoprophylaxis, or the use of antiretroviral drugs in preexposure prophylaxis (PrEP) for HIV disease. Most research is focused on the use of tenofovir disoproxil fumarate (TDF) [2, 3], because of its long half-life, its excellent safety record, and the findings of earlier research that involved nonhuman primates and showed some efficacy in the prevention of infection by simian immunodeficiency virus (SIV) [4–6]. The new study by Subbarao et al. [1] used a more stringent model to study viral transmission and suggested that TDF may not be sufficient for the prevention of HIV disease when used as a single agent The study by Subbarao et al. [1] set the bar much higher for chemoprophylaxis agents, by using repeated viral challenges weekly for 14 weeks. Repeated exposures may simulate the experience of sexually active people who are exposed to HIV through sexual contact with ⩾1 partner. Of 12 macaques studied, 2 groups of 4 macaques each received TDF via an oral route, either once daily or once weekly, whereas the remaining 4 macaques served as untreated control animals. The results revealed that the control animals became infected at a median of 1.5 weeks after initiation of the study, whereas the groups receiving TDF once daily or once weekly became infected after a median duration of 6 weeks and 7 weeks, respectively. At the end of 14 weeks, all animals were infected
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.070 | 0.072 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".