Early life predictors of childhood intelligence: findings from the Mater‐University study of pregnancy and its outcomes
Bibliographic record
Abstract
Summary Lawlor DA, Najman JM, Batty GD, O’Callaghan MJ, Williams GM, Bor W. Early life predictors of childhood intelligence: findings from the Mater‐University study of pregnancy and its outcomes. Paediatric and Perinatal Epidemiology 2006; 20: 148–162. Growing evidence linking childhood intelligence with adult health outcomes suggests a need to identify predictors of this psychological characteristic. In this study, we have examined the early life determinants of childhood intelligence in a population‐based birth cohort of individuals born in Brisbane, Australia between 1981 and 1984. In univariable analyses, family income in the year of birth, maternal and paternal education, maternal age at birth, maternal ethnicity, maternal smoking during pregnancy, duration of labour, birthweight, breast feeding and childhood height, and body mass index were all associated with intelligence at age 14. In multivariable analyses, the strongest and most robust predictors of intelligence were family income, parental education and breast feeding, with these three variables explaining 7.5% of the variation in intelligence at age 14. Addition of other variables added little further explanatory power. Our results demonstrate the importance of indicators of socio‐economic position as predictors of intelligence, and illustrate the need to consider the role of such factors in generating the association of childhood intelligence with adult disease 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".