Early Growth and Impacts on Long-Term Neurodevelopment and Human Capital
Bibliographic record
Abstract
BACKGROUND: Growth trajectories during the first 1,000 days from conception to 2 years influence human capital, predicting intelligence, skills and health in adults. SUMMARY: This review describes current evidence on the impacts of adverse pregnancy outcomes such as low birth weight, preterm birth, small for gestational age, and infant nutrition on long-term neurodevelopment and summarizes interventions that have proven to be effective in improving child development and further impact human capital. To date, no globally standardized measurements of child development in low-medium-income countries exist, and comparisons among studies using different developmental scales are challenging. In the perinatal period, birth weight, gestational age at delivery and elevated placental blood flow resistance have been identified as the main risk factors for global neurological delay, poor neurosensory development and cerebral palsy. Although these adverse neurological outcomes have decreased in developed settings, it is still a problem in low-resource populations. Nutritional deficiencies are the main drivers of developmental impairment, notably iron, iodine and folate deficiencies, and environmental stressors during pregnancy such as air pollution, exposure to chemicals, substance abuse, smoking, and maternal/parental psychiatric disorders can affect the developing brain. Interventions aiming to improve maternal macro- and micronutrient status, delayed cord clamping, exclusive breastfeeding and nurturing care have demonstrated to be effective strategies to prevent perinatal complications known to affect child development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".