Screening for Dysglycemia in Overweight Youth Presenting for Weight Management
Bibliographic record
Abstract
OBJECTIVE: To examine the performance of current screening recommendations for detecting dysglycemia in children and adolescents with obesity. RESEARCH DESIGN AND METHODS: In a cross-sectional study, an oral glucose tolerance test and demographic (age, sex, family history of diabetes, and ethnicity), clinical (BMI z score, waist circumference, and pubertal stage), and laboratory variables used in current pediatric screening criteria for type 2 diabetes mellitus were measured in 259 overweight or obese youth aged 5-17 years. Glycemic status was based on American Diabetes Association (ADA) thresholds. The performance (sensitivity and specificity) of current screening criteria and newly developed models to identify isolated IGT were compared. RESULTS: Dysglycemia was present in 20.8% of the cohort. Of the 54 participants with dysglycemia, 68% had a normal fasting glucose and were identified with the 2-h glucose test. Current ADA criteria had low sensitivity (41.7% [95% CI 25.6-57.8]) and moderate specificity (69.5% [63.5-75.6]) to identify IGT. In receiver operating characteristic (ROC) analysis, the addition of hemoglobin A(1c) (HbA(1c)) or FPG did not improve the ROC area under the curve (AUC) (HbA(1c): 0.64 vs. 0.63; P = 0.54; HbA(1c) + FPG: 0.66; P = 0.42), but adding triglyceride level did (AUC 0.72 vs. 0.63; P = 0.03). A simple model with fasting triglyceride level >1.17 mmol/L improved AUC compared with ADA screening criteria (0.68 vs. 0.57; P = 0.04). CONCLUSIONS: The prevalence of IGT is high among obese children and youth. Current screening criteria have low sensitivity to detect isolated IGT. Although adding nonfasting laboratory values to history and physical measures does not improve diagnostic accuracy, adding fasting lipid profile improves predictive value.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".