In Utero Antiretroviral Exposure and Risk of Neurodevelopmental Problems in HIV-Exposed Uninfected 5-Year-Old Children
Bibliographic record
Abstract
Studies have observed neurodevelopmental (ND) challenges among young children perinatally HIV-exposed yet uninfected (CHEU) with in utero antiretroviral (ARV) exposure, without clear linkage to specific ARVs. Atazanavir (ATV) boosted with ritonavir has been a preferred protease inhibitor recommended for pregnant women, yet associations of ATV with ND problems in CHEU have been reported. Studies among early school-age children are lacking. The pediatric HIV/AIDS cohort study (PHACS) surveillance monitoring for antiretroviral therapy (ART) toxicities (SMARTT) study evaluated 5-year-old monolingual English-speaking CHEU using the behavior assessment system for children, Wechsler preschool and primary scales of intelligence, and test of language development-primary. A score ≥1.5 standard deviations worse than population norms defined a signal within each domain. Analyses of risk for signals were stratified by timing of any ARV initiation. Associations between ARV exposure and risk of ND signals were assessed using proportional odds models, adjusting for confounders. Among 230 children exposed to ARVs at conception, 15% had single and 8% had multiple ND problems; ATV exposure was not associated with higher risk of signals [adjusted cumulative odds ratio (cOR) = 0.66, confidence interval (CI): 0.28-1.56]. However, among 461 children whose mothers initiated ARVs during pregnancy, 21% had single and 12% had multiple ND problems; ATV exposure was associated with higher risk of signals (cOR = 1.70, CI: 0.82-3.54). The specific regimen tenofovir/emtricitabine/ATV was associated with higher risk (cOR = 2.31, CI: 1.08-4.97) relative to regimens using a zidovudine/lamivudine backbone combined with non-ATV ARVs. It remains important to monitor neurodevelopment of CHEU during early childhood and investigate the impact and the role of timing of in utero exposure to specific ARVs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".