53 The association between maternal preconception BMI and child zBMI growth rates
Bibliographic record
Abstract
Abstract Background Elevated body mass index (BMI) and rapid growth in early childhood are important predictors of obesity risk. While studies have identified an association between maternal preconception BMI and child BMI z-scores (zBMI), the association with zBMI growth rates during distinct growth periods is unclear. Objectives To assess the association between maternal preconception BMI and child zBMI growth rates and BMI z-scores, in children aged 0 to 10 years old. Additionally, to examine whether child sex and gestational weight gain (GWG) modify these associations. Design/Methods This longitudinal study consisted of healthy children (0 to 10 years) enrolled in a primary care practice-based research cohort. The exposure was maternal BMI measured during the preconception period, defined as the 2 years prior to pregnancy. The primary outcome was child zBMI growth, modeled with piecewise linear splines for age. The secondary outcome was repeated measures of child BMI z-scores. Piecewise linear mixed models were used to determine the association with growth, while linear mixed models were used for zBMI. Effect modification by child sex and GWG was explored. Results 499 children were included. Maternal preconception BMI had a small, but statistically significant association with child zBMI growth rates during some growth periods, with the strongest association from 0 to 4 months (0.007; p=0.004). Maternal preconception BMI was also associated with child zBMI; 1kg/m2 increase was associated with 0.03 zBMI increase (p=0.001). Child sex and maternal GWG did not modify these associations. Conclusion There is evidence to suggest an association between maternal preconception BMI and child zBMI growth and scores. In addition to maternal and pregnancy benefits, preconception interventions may have longer-term benefits for child 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".