Normal Birth Weight Variation and Children’s Neuropsychological Functioning: Links between Language, Executive Functioning, and Theory of Mind
Bibliographic record
Abstract
The effect of low birth weight on children's development has been documented for a range of neurocognitive outcomes. However, few previous studies have examined the effect of birth weight variability within the normal range on children's neuropsychological development. The current study examined birth weight variation amongst children weighing ≥2500 g in relation to their language, executive functioning (EF), and theory of mind (ToM), and specified a developmental pathway in which birth weight was hypothesized to be associated with children's EF and ToM through their intermediary language skills. The current study used a prospective community birth cohort of 468 children. Families were recruited when children were newborns and followed up every 18 months until children were age 4.5. Language was assessed at age 3 using a standardized measure of receptive vocabulary (PPVT), and EF and ToM were measured at age 4.5 using previously validated and developmentally appropriate tasks. After controlling for potential confounding variables (family income, parent education, gestational age), birth weight within the normal range was associated with language ability at age 3 (β=.17; p=.012); and the effect of birth weight on both EF (z=2.09; p=.03) and ToM (z=2.07; p=.03) at age 4.5 operated indirectly through their language ability at age 3. Our findings indicate that the effects of birth weight on child neurocognition extend into the normal range of birth weight, and specific developmental mechanisms may link these skills over time.
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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 | 0.005 |
| 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 | 0.000 |
| 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".