Relationship between BMI and adiposity among different ethnic groups in 2-year-old New Zealand children
Bibliographic record
Abstract
Age- and sex-based BMI cut-offs are used to define overweight and obesity, but the relationship between BMI and body composition has not been very well studied in children or compared between children of different ethnic groups. Body size and composition in childhood are also influenced by size at birth. Our aim was to compare body size and composition at 2 years in children with different ethnicity and size at birth. We prospectively followed a multi-ethnic cohort of 300 children born with risk factors for neonatal hypoglycaemia (infants of diabetics, large or small at birth or late preterm) to 2 years corrected age. Complete data on weight, height and head circumference and body composition using bioelectrical impedance 24±1 months corrected age were available in 209 children. At birth, compared with European children, Chinese, Indian and other ethnicity children were lighter, and Indian children had smaller head circumferences, but birth lengths were similar in all ethnic groups. At 2 years, Pacific children were heavier and had higher BMI z scores, and Indian children had smaller head circumferences and lower BMI z scores than those from other ethnic groups. However, fat mass and fat-free mass indices were similar in all groups. At median BMI, fat mass:fat-free mass ratio was 23 % lower in Pacific than in Indian children (0·22 v. 0·27, P=0·03). BMI is not a good indicator of adiposity in this multi-ethnic cohort of 2-year-old New Zealand children.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".