Early childhood body mass index growth and school readiness: A longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Child growth influences future health and learning. School readiness refers to a child's ability to meet developmental expectations at school entry. The association of early growth rate and patterns with school readiness remains unknown. OBJECTIVE: To determine the association of child body mass index (BMI) growth with school readiness in a cohort of young children. METHODS: A prospective cohort study (2015-2022) was conducted in children 0-6 years enrolled in the TARGet Kids! research network in Toronto, Canada. Two analytical approaches were used to measure growth using child weight and height/length data between 0 and 4 years: (i) age- and sex-standardised BMI (zBMI) growth rate per year using a piecewise linear model; and (ii) distinct zBMI trajectories using latent class mixed models. School readiness (4-6 years) was measured using teacher-completed Early Development Instrument (EDI). Robust Poisson models and marginal linear models using generalised estimating equations were used adjusting for confounders identified a priori. RESULTS: In this study of 1077 children (mean age at EDI completion: 4.8 years; 52.6% male) with 6415 zBMI measurements, mean growth rate was 0.65 zBMI units/year (0-2 years) and -0.11 zBMI units/year (2-4 years). Two distinct zBMI trajectories were identified: the stable trajectory and the catch-up trajectory. There was insufficient evidence that zBMI growth rates (risk ratio 1.10, 95% confidence interval 0.78, 1.55 for 0-2 years; risk ratio 0.71, 95% confidence interval 0.32, 1.57 for 2-4 years) or trajectories (risk ratio 1.05, 95% confidence interval 0.82, 1.35, catch-up trajectory vs. stable trajectory) were associated with school readiness. CONCLUSIONS: No association was found between BMI growth and school readiness. School readiness may be more impacted by factors directly related to obesity or adiposity at the time of EDI measurement rather than growth.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".