More than just body mass index: Using the Edmonton obesity staging system for pediatrics to define obesity severity in a multi‐ethnic Australian pediatric clinical cohort
Bibliographic record
Abstract
Background: Despite advancements in the use of body mass index (BMI) to categorize obesity severity in pediatrics, its utility in guiding individual clinical decision making remains limited. The Edmonton Obesity Staging System for Pediatrics (EOSS-P) provides a way to categorize the medical and functional impacts of obesity according to the severity of impairment. The aim of this study was to describe the severity of obesity among a sample of multicultural Australian children using both BMI and EOSS-P tools. Methods: This cross-sectional study included children aged 2-17 years receiving obesity treatment through the Growing Health Kids (GHK) multi-disciplinary weight management service in Australia between January to December 2021. BMI severity was determined using the 95th percentile for BMI on age and gender standardized Centre for Disease Control and Prevention (CDC) growth charts. The EOSS-P staging system was applied across the four health domains (metabolic, mechanical, mental health and social milieu) using clinical information. Results: Complete data was obtained for 338 children (age 10.0 ± 3.66 years), of whom 69.5% were affected by severe obesity. An EOSS-P stage 3 (most severe) was assigned to 49.7% of children, the remaining 48.5% were assigned stage 2 and 1.5% were assigned stage 1 (least severe). BMI predicted health risk as defined by EOSS-P overall score. BMI class did not predict poor mental health. Conclusion: Used in combination, BMI and EOSS-P provide improved risk stratification of pediatric obesity. This additional tool can help focus resources and develop comprehensive multidisciplinary treatment plans.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".