Does Genetic Diversity of HIV-1 Non-B Subtypes Differentially Impact Disease Progression in Treatment-Naive HIV-1–Infected Individuals? A Systematic Review of Evidence
Bibliographic record
Abstract
With 88% of HIV-1-infected individuals living in areas of high prevalence of non-B subtypes and with expanded global access to antiretroviral treatment (ART), studying disease progression amongst non-B subtypes gains relevance. Optimized clinical management is a possibility with knowledge of non-B subtype profiles at baseline, which is currently not possible due to lack of subtype-specific point-of-care assays. In a systematic review, we synthesized global evidence on differential disease progression amongst non-B subtypes in ART-naive individuals. Due to lack of consistent effect measures, we avoided pooling data and inferred patterns with respect to disease progression outcomes (ie, AIDS, Death, CD4, viral load changes). Subtypes C and D were more aggressive, followed by G, AE, and AG, and A being the least aggressive of all HIV-1 subtypes. Evidence of greater rates of disease progression in globally prevalent C and D subtypes highlight the importance of expanding early HIV detection, and determining subtype profile at baseline with CD4 staging to optimize the quality of ART delivery and care in global settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".