Neurodevelopment, Nutrition, and Inflammation: The Evolving Global Child Health Landscape
Bibliographic record
Abstract
The last decade has witnessed major reductions in child mortality and a focus on saving lives with key interventions targeting major causes of child deaths, such as neonatal deaths and those due to childhood diarrhea and pneumonia. With the transition to Sustainable Development Goals, the global health community is expanding child health initiatives to address not only the ongoing need for reduced mortality, but also to decrease morbidity and adverse exposures toward improving health and developmental outcomes. The relationship between adverse environmental exposures frequently associated with factors operating in the prepregnancy period and during fetal development is well established. Also well appreciated are the developmental impacts (both short- and long-term) associated with postnatal factors, such as immunostimulation and environmental enteropathy, and the additional risks posed by the confluence of factors related to malnutrition, poor living conditions, and the high burden of infections. This article provides our current thinking on the pathogenesis and risk factors for adverse developmental outcomes among young children, setting the scene for potential interventions that can ameliorate these adversities among families and children at risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".