Neurological development of children who are HIV‐exposed and uninfected
Bibliographic record
Abstract
Widespread use of antiretroviral drugs for pregnant/breastfeeding females with human immunodeficiency virus (HIV) has led to declining vertical transmission. Despite being HIV-uninfected, the increasing number of children who are HIV-exposed and uninfected (CHEU) often present with developmental alterations. We review seminal and recent evidence on the neurological development of CHEU and associations with early life HIV/antiretroviral exposure. Our conceptual model highlights the numerous exposures and universal risk factors for CHEU developmental disorders. Early studies suggest a significant association between HIV exposure and neurological abnormalities, varying according to the burden of HIV-specific exposures and other risk factors. More recent observations from the modern era are inconsistent, although some studies suggest specific antiretrovirals may adversely affect neurological development of CHEU. As the CHEU population continues to grow, alongside simultaneous increases in types and combinations of antiretrovirals used in pregnancy, long-term monitoring of CHEU is necessary for understanding the effects of HIV/antiretroviral exposure on CHEU developmental outcomes. What this paper adds Evidence on the neurological development of children who are human immunodeficiency virus (HIV)-exposed and uninfected (CHEU) is synthesized. Comparisons are made to children who are HIV-unexposed, across treatment eras and settings, and by antiretroviral drug regimens and drug classes. CHEU exposures are complex and include HIV-specific and universal risk factors which may affect development during the early years of life.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".