The Predictive Value of Head Circumference Growth during the First Year of Life on Early Child Traits
Bibliographic record
Abstract
Atypical head circumference (HC) growth has been associated with neurodevelopmental disorders. However, whether it is associated with specific aspects of development in early childhood in the general population is unknown. The objective of this study was to assess the predictive value of HC growth as an early biomarker of behavioral traits. We examined longitudinal associations between HC growth from 0 to 12 months and temperament, cognitive, and motor development at 24 months. A subsample of healthy children (N = 756) was drawn from the 3D (Design, Develop, Discover) cohort study. Early HC growth was modeled with latent growth curve analysis. Greater postnatal HC growth predicted lower temperamental effortful control and lower surgency/extraversion in boys. HC growth did not predict cognitive or fine motor scores, but did predict greater gross motor skills in boys. No significant effect of HC growth was found in girls. This study is the first to demonstrate an association between postnatal HC growth and specific aspects of child development in a healthy population. Results suggest HC growth overshadows brain mechanisms involved in behavioral traits in early infancy. Whether links are maintained throughout development and the mechanisms involved correspond to traits found in atypical populations remains to be studied.
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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 | 0.006 |
| 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 | 0.000 | 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".