The Influence of Birth Size on Intelligence in Healthy Children
Bibliographic record
Abstract
OBJECTIVE: Birth parameters have been hypothesized to have an influence on IQ. However, studies within the range of normal birth size have been sparse. With this study we examined the associations between birth length, birth weight, head circumference, and gestational age within the normal birth size range in relation to childhood IQ in Asian children. METHODS: A cohort of 1979 of 2913 Asian children aged 7 to 9 years, recruited from 3 schools in Singapore, were followed yearly from 1999 onward. Birth parameters were recorded by health personnel. Childhood IQ was measured with the Raven's Standard Progressive Matrices at ages 8 to 12. RESULTS: The mean IQ score across the sample (n = 1645) was 114.2. After controlling for multiple confounders for every 1-cm increment in birth length, 1 kg in birth weight, or 1 cm in head circumference, there was a corresponding increase in IQ of 0.49 points (P for trend < .001), 2.19 points (P for trend = .007) and .62 points (P for trend = .003), respectively. These associations persisted even after exclusion of premature children and children with extreme weights and head circumferences. CONCLUSIONS: Longer birth length, higher birth weight, or larger head circumferences within the normal birth size range are associated with higher IQ scores in Asian children. Our results suggest that antenatal factors reflected in altered rates of growth but within the normative range of pregnancy experiences play a role in generating cognitive potential. This has implications for targeting early intervention and preventative programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".