Measuring severe obesity in pediatrics: A cohort study
Bibliographic record
Abstract
Abstract Purpose: To examine cross-sectional and longitudinal relationships between body mass index (BMI)-derived metrics for measuring severe obesity (SO) using the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) references and cardiometabolic risk factors in children and adolescents. Methods: In this cohort study completed from 2013 to 2021, we examined data from 3- to 18-year-olds enrolled in the CANadian Pediatric Weight management Registry. Anthropometric data were used to create nine BMI-derived metrics based on the CDC and WHO references. Cardiometabolic risk factors were examined, including dysglycemia, dyslipidemia, and elevated blood pressure. Analyses included intraclass correlation coefficients (ICC) and receiver operator characteristic area-under-the-curve (ROC AUC). Results: Our sample included 1,288 participants (n=666 [51.7%] girls; n=874 [67.9%] white), with SO of 59.9–67.0%. ICCs revealed high tracking (0.90–0.94) for most BMI-derived metrics. ROC AUC analyses showed CDC and WHO metrics discriminated the presence of cardiometabolic risk factors, which improved with increasing numbers of risk factors. Overall, most BMI-derived metrics rated poorly in identifying presence of cardiometabolic risk factors. Conclusion: CDC BMI percent of the 95th percentile and WHO BMIz performed similarly as measures of SO, suggesting both can be used for clinical care and research in pediatrics. The latter definition may be particularly useful for clinicians and researchers from countries that recommend using the WHO growth reference.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".