Brief Report: Herpesvirus Viremia, ART Response, and Growth Recovery in Kenyan Children Starting Antiretroviral Therapy in Hospital
Bibliographic record
Abstract
BACKGROUND: We recently reported that cytomegalovirus (CMV) viremia was associated with worse clinical outcomes in hospitalized children starting antiretroviral therapy (ART). Here, we interrogate associations between CMV and Epstein-Barr (EBV) viremia and HIV viral suppression, CD4 recovery, and growth after ART initiation. SETTING: In-patient setting, 4 hospitals in Kenya. METHODS: We used regression models to assess associations between CMV and EBV viremia and 6-month growth and immunologic and virologic responses to ART in children aged 2 months to 12 years. RESULTS: Overall, 142 (78%) of 181 children survived 6 months, of whom 94 (66%) had CMV and EBV DNA assessments pre-ART. Adjusting for baseline HIV RNA, children with baseline plasma CMV DNA ≥1000 IU//mL (coefficient=-5.65; 95% CI: -10.9 to -0.41, P = 0.035) and children with EBV DNA ≥1000 copies/mL had slower rates of CD4% increase (coefficient=-4.66; 95% CI: -7.06 to -2.27, P =<0.001). Age, WHO clinical stage, and stunting predicted growth recovery. CONCLUSIONS: High CMV or EBV viremia at admission predicted slower CD4 gain over 6 months in infants starting ART. These data suggest that herpesvirus viremia at baseline may identify a subset of children who will have a slower immunologic response to ART. Further research is needed to determine whether herpesvirus viremia delays ART responses and recovery in children.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".