Fighting the Public Health Burden of AIDS With the Human Pegivirus
Bibliographic record
Abstract
Highly active antiretroviral therapy has revolutionized the battle against human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). From its current global rollout, HIV/AIDS morbidity and mortality has been greatly reduced, yet there exists substantial interest in the development of new therapies to further mitigate the HIV/AIDS health burden and to inhibit any fallout from the development of antiretroviral drug resistance. One potential intervention is the human pegivirus (HPgV). HPgV is not known to cause disease, and most remarkably it is shown to delay the progression of HIV to AIDS. However, the health benefit of increasing HPgV prevalence in the community of HIV-infected men remains unknown at the public health level. We evaluated the utility of HPgV biovaccination for mitigating the HIV/AIDS health burden using mathematical models. Importantly, our work considers the potential concern that HPgV will, itself, evolve to become disease-causing by permitting mutant disease-causing HPgV strains to potentially arise during treatment. Our findings show that HPgV biovaccination rates of 12.5%-50% annually could prevent 4.2-23.6 AIDS incidences and 3.3-18.8 AIDS deaths, and could save 2.9-18.6 disability-adjusted life years per 1,000 people. Together, these findings indicate that HPgV biovaccination could be an effective therapy for reducing HIV/AIDS morbidity and mortality, and thus warrants further exploration.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".