Metabolically healthy obesity in children enrolled in the <scp>CANadian</scp> Pediatric Weight management Registry (<scp>CANPWR</scp>): An exploratory secondary analysis of baseline data
Bibliographic record
Abstract
Our study purpose was to determine the prevalence of metabolically healthy obesity (MHO) and examine factors associated with MHO in children with obesity. This cross-sectional study was a secondary, exploratory analysis of data that included 2-17 years old with a body mass index (BMI) ≥85th percentile from the CANadian Pediatric Weight management Registry. Children were classified as having MHO or metabolically unhealthy obesity (MUO) using consensus-based criteria. Those with MHO had normal triglycerides, high-density lipoprotein cholesterol, blood pressure, and fasting glucose. Logistic regression was used to examine factors associated with MHO, which included calculating odds ratios (ORs) and 95% confidence intervals (CIs). In total, 945 children were included (mean age: 12.3 years; 51% female). The prevalence of MHO was 31% (n = 297), with lower levels across increasing age categories (2-5 years [n = 18; 43%], 6-11 years [n = 127; 35%], 12-17 years [n = 152; 28%]). Children with MHO were younger, weighed less, and had lower BMI z-scores than their peers with MUO (all p < 0.01). MHO status was positively associated with physical activity (OR: 1.18; 95% CI: 1.01-1.38), skim milk intake (OR: 1.10; 95% CI: 1.01-1.19), and fruit intake (OR: 1.12; 95% CI: 1.01-1.24) and negatively associated with BMI z-score (OR: 0.69; 95% CI: 0.60-0.79), total screen time in hours (OR: 0.79; 96% CI: 0.68-0.92), and intake of fruit flavoured drinks (OR: 0.91; 95% CI: 0.84-0.99). These findings may help guide clinical decision-making regarding obesity management by focusing on children with MUO who are at relatively high cardiometabolic risk.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".