How do maternal HIV infection and the early nutritional environment influence the development of infants exposed to HIV <i>in utero</i> ?
Bibliographic record
Abstract
Abstract Malnutrition and infectious disease often coexist in socially inequitable contexts. Malnutrition in the perinatal period adversely affects offspring development and lifelong non-communicable disease risk. Less is known about the effects of infectious disease exposure during critical windows of development and health, and links between in utero HIV-exposure in the absence of neonatal infection, perinatal nutritional environments, and infant development are poorly defined. In a pilot feasibility study at Kalafong Hospital, Pretoria, South Africa, we aimed to better understand relationships between maternal HIV infection and the early nutritional environment of in utero HIV exposed uninfected (HEU) infants. We also undertook exploratory analyses to investigate relationships between food insecurity and infant development. Mother-infant dyads were recruited after delivery and followed until 12 weeks postpartum. Household food insecurity, nutrient intakes and dietary diversity scores did not differ between mothers living with or without HIV. Maternal reports of food insecurity were associated with lower maternal nutrient intakes 12 weeks postpartum, and in infants, higher brain-to-body weight ratio at birth and 12 weeks of age, and attainment of fewer large movement and play activities milestones at 12 weeks of age, irrespective of maternal HIV status. Reports of worry about food runout were associated with increased risk of stunting for HEU, but not unexposed, uninfected infants. Our findings suggest that food insecurity, in a vulnerable population, adversely affects maternal nutritional status and infant development. In utero exposure to HIV may further perpetuate these effects, which has implications for early child development and lifelong human capital.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".