Early life trajectories of head circumference predict executive function and fluid cognitive skills at age 4 in Kenya
Bibliographic record
Abstract
Abstract Head circumference (HC) is a low-cost proxy for early brain development, yet few studies have examined its predictive value for specific neurocognitive outcomes in low- and middle-income countries. This study investigated whether trajectories of HC growth from 1 to 24 months predict executive function and fluid cognitive skills at age 4 in a Kenyan cohort ( N = 182). Using latent growth curve modeling, we found that greater HC growth was significantly associated with better EF and fluid cognitive skills, independent of initial HC and sociodemographic factors. These associations were robust across subgroups defined by prenatal exposure to HIV and atypical physical growth (i.e., extreme values for weight-for-length, underweight, or HC). Moreover, the predictive association between early HC and later neurocognition was evident within the first 15 months of life. This study highlights the value of monitoring changes in HC as one aspect of early child health and wellbeing. Infants who do not exhibit normative increases in HC in infancy may benefit from early neurocognitive assessments and/or the receipt of early intervention services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".