HIV treatment as prevention: The key to an AIDS-free generation
Bibliographic record
Abstract
The presence of elevated human immunodeficiency virus (HIV) viral load within blood and genital secretions is a critical driver of transmission events. Long-term suppression of the viral load to undetectable levels through the use of antiretroviral therapy is standard practice for the clinical management of HIV. Antiretroviral therapy therefore can play a key role in curbing HIV transmission. The results of a randomized clinical trial and several observational studies have now confirmed that antiretroviral therapy markedly decreases the risk of HIV transmission. Mathematical models and population-based ecologic studies suggest that further expansion of antiretroviral coverage within current guidelines can play a major role in controlling the spread of HIV. The expansion of so-called “treatment as prevention” initiatives relies on maximal uptake of the HIV continuum-of-care cascade to allow for the successful identification of people who are not yet known to be HIV-infected, for engaging patients in appropriate care, and for subsequently achieving sustained virologic suppression in patients with the use of antiretroviral therapy. Since 2010, the joint United Nations AIDS (UNAIDS) program has called for the inclusion of antiretroviral treatment as a key pillar in the global strategy to control the spread of HIV infection. This has now been invigorated by the release of the World Health Organization's 2013 Consolidated Antiretroviral Therapy Guidelines, which recommends offering treatment to all HIV-infected individuals with CD4 cell counts below 500/mm3, to serodiscordant couples, to individuals coinfected with tuberculosis and hepatitis B virus, to pregnant women, and to children below the age of 5 years (regardless of CD4 cell count in this groups).
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".