Global Response to HIV: Treatment as Prevention, or Treatment for Treatment?
Bibliographic record
Abstract
The concept of "treatment as prevention" has emerged as a means to curb the global HIV epidemic. There is, however, still ongoing debate about the evidence on when to start antiretroviral therapy in resource-poor settings. Critics have brought forward multiple arguments against a "test and treat" approach, including the potential burden of such a strategy on weak health systems and a presumed lack of scientific support for individual patient benefit of early treatment initiation. In this article, we highlight the societal and individual advantages of treatment as prevention in resource-poor settings. We argue that the available evidence renders the discussion on when to start antiretroviral therapy unnecessary and that, instead, efforts should be aimed at offering treatment as soon as possible.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".