Effects of Human Leukocyte Antigen Class I Genetic Parameters on Clinical Outcomes and Survival after Initiation of Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Human leukocyte antigen (HLA) class I variation influences the progression of untreated human immunodeficiency virus (HIV) disease; however, it is not known whether HLA class I variation may influence clinical outcomes after initiation of highly active antiretroviral therapy (HAART). METHODS: Associations between HLA class I genotypes and pretherapy clinical parameters were investigated in a cohort of 765 antiretroviral-naive adults initiating HAART. Cox proportional hazards regression was used to investigate the effects of HLA class I genotypes on time to suppression of the viral load to <500 HIV RNA copies/mL, time to an increase in the CD4 cell count to >100 cells/mm(3) above the baseline count, and time to nonaccidental death over a >5-year period after initiation of HAART. RESULTS: Homozygosity at any HLA class I locus and possession of common HLA alleles were associated with a higher pretherapy viral load (P<.05). In multivariate analyses controlling for sociodemographic and clinical parameters at baseline, HLA class I homozygosity was significantly associated with a poorer CD4 cell response (P=.008), whereas possession of uncommon HLA alleles was associated with slower virologic suppression after initiation of HAART (P=.02). We observed no significant association between HLA parameters and time to nonaccidental death after initiation of HAART (P>.05, univariate analysis). CONCLUSION: HLA class I zygosity-dependent and frequency-dependent effects may influence short-term HAART outcomes, and, thus, they deserve further investigation. No effects of these HLA parameters on survival after initiation of HAART were observed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".