The Edmonton Obesity Staging System for Pediatrics (EOSS-P) in Mexican Children and Adolescents Living with Obesity: Beyond BMI Obesity Classes
Bibliographic record
Abstract
Background/Objectives: The Edmonton Obesity Staging System (EOSS) was developed to stage the obesity in adult populations. Subsequently, this staging system was designed for pediatric populations (EOSS-P). This study aimed to describe obesity severity using EOSS-P and correlate it with BMI classes in Mexican children and adolescents living with obesity. Methods: This is a cross-sectional analysis carried out with data from school-age children and adolescents living with obesity who were referred to the Pediatric Obesity Clinic at the Child Welfare Unit at the General Hospital of Mexico “Dr. Eduardo Liceaga”. Obesity was staged using the EOSS-P. To evaluate the association between obesity classes and each EOSS-P domain, as well as overall EOSS-P staging, we performed Bayesian ordered logistic regression models. Results: A total of 118 participants were included; 43.2% were female and 56.8% were male. Based on the overall EOSS-P staging, 56.8% of participants were classified as stage 3, while none were categorized as stage 0. Obesity class II-III was associated with higher odds for the mechanical (OR = 2.5), metabolic (OR = 1.9), and social (OR = 1.6) domains. Conclusions: Pediatric obesity assessment should extend beyond BMI to include the evaluation of metabolic, mechanical, and psychological domains, identifying health complications and barriers that may impact treatment effectiveness and adherence. The EOSS-P is a valuable tool for staging pediatric obesity based on these domains and can guide personalized clinical decision-making.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".