Do HIV‐1 non‐B subtypes differentially impact resistance mutations and clinical disease progression in treated populations? Evidence from a systematic review
Bibliographic record
Abstract
There are 31 million adults living with HIV-1 non-B subtypes globally, and about 10 million are on antiretroviral therapy (ART). Global evidence to guide clinical practice on ART response in HIV-1 non-B subtypes remains limited. We systematically searched 11 databases for the period 1996 to 2013 for evidence. Outcomes documented included time to development of AIDS and/or death, resistance mutations, opportunistic infections, and changes in CD4 cell counts and viral load. A lack of consistent reporting of all clinical end points precluded a meta-analysis. In sum, genetic diversity that precipitated differences in disease progression in ART-naïve populations was minimized in ART-experienced populations, although variability in resistance mutations persisted across non-B subtypes. To improve the quality of patient care in global settings, recording HIV genotypes at baseline and at virologic failure with targeted non-B subtype-based point-of-care resistance assays and timely phasing out of resistance-inducing ART regimens is recommended.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".