Impact of Neonatal Growth on IQ and Behavior at Early School Age
Bibliographic record
Abstract
OBJECTIVES: The objective was to examine associations of neonatal weight gain (NWG) and head circumference gain (HCG) with IQ scores and behavior at early school age. METHODS: We used data from the Promotion of Breastfeeding Intervention Trial, involving Belarusian infants born full term and weighing ≥2500 g. NWG and HCG were measured as the percentage gain in weight and head circumference over the first 4 weeks relative to birth size. IQ and behavior were measured at 6.5 years of age by using the Wechsler Abbreviated Scales of Intelligence and the Strengths and Difficulties Questionnaire (SDQ), respectively, with SDQ collected from parents and teachers. The associations between the exposures (NWG, HCG) and children's IQ and SDQ were examined by using mixed models to account for clustering of measurements, and adjustment for potentially confounding perinatal and socioeconomic factors. RESULTS: Mean NWG was 26% (SD 10%) of birth weight. In fully adjusted models, infants in the highest versus lowest quartile of NWG had 1.5-point (95% confidence interval [CI] 0.8 to 2.2) higher IQ scores (n = 13 840). A weak negative (protective) association between NWG and SDQ total difficulties scores was observed for the teacher-reported (β = -0.39, 95% CI -0.71 to -0.08, n = 12 016), but not the parent-reported (β = -0.12, 95% CI -0.39 to 0.15, n = 13 815), SDQ. Similar associations were observed with HCG and IQ and behavior. CONCLUSIONS: Faster gains in weight or head circumference in the 4 weeks after birth may contribute to children's IQ, but reverse causality (brain function affects neonatal growth) cannot be excluded.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".