Plasma HIV-1 RNA to Guide Patient Selection for Antiretroviral Therapy in Resource-Poor Settings
Bibliographic record
Abstract
Scaling up access to highly active antiretroviral therapy (HAART) requires eligibility criteria that safeguard treatment efficiency in resource-poor settings. We determined whether supply of HAART on the basis of plasma viral load testing could result in a stronger reduction of AIDS incidence as compared with CD4 count-driven strategies. Expected AIDS incidence rates corresponding to distinct HAART eligibility criteria were calculated by relying on risk parameters obtained through the Amsterdam cohort studies on HIV infection and AIDS. We modeled 2 different treatment settings derived from sub-Saharan African surveys. In a hospital-based setting, the reduction in the 1-year AIDS incidence is the same for any HAART administration rate if patients are selected on a single CD4 cell count criterion or on (additional) criteria for plasma HIV-1 RNA. In a community-based setting, where patients are identified at less advanced stages of infection, the reduction in the 1-year AIDS incidence is higher at particular HAART administration rates if patients are selected on criteria for plasma HIV-1 RNA rather than CD4 cell count. Plasma viral load testing can ensure a more efficient allocation of antiretroviral therapy but only when applied to a strategy of active case finding in the community.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".