Insulin resistance and the metabolic syndrome are related to the severity of steatosis in the pediatric population with obesity
Bibliographic record
Abstract
BACKGROUND: To determine the factors associated with an increased risk for severe steatosis (SS) and establish the Homeostatic Model Assessment-Insulin Resistance (HOMA-IR) as a screening tool. METHODS: A cross-sectional study was performed in obese children to assess the relationship between the metabolic syndrome (MetS) and glucose metabolism alterations (GMA) and the risk for severe steatosis. RESULTS: A total of 94 children (51 males) aged from six to 14 years were included. Thirteen children (14.8%) had severe steatosis (SS). The anthropometric variables associated with SS included body mass index (BMI) (SS 34.1 vs non-SS 29.7, p = 0.005), waist circumference (cm) (100 vs 92.5, p = 0.015) and hip circumference (cm) (108 vs 100, p = 0.018). The blood parameters included alanine aminotransferase (ALT) (UI/dl) (27 vs 21, p = 0.002), gamma-glutamil transpeptidase (GGT) (UI/dl) (16 vs 15, p = 0.017), fasting glycemia (mg/dl) (96 vs 88, p = 0.006), fasting insulin (UI/dl) (25 vs 15.3, p < 0.001) and HOMA-IR score (7.1 vs 3.7, p < 0.001). Eighteen children with MetS were found to be at an increased risk for severe steatosis (odds ratio [OR] 11.36, p < 0.001). After receiver operating characteristic (ROC) curve analysis, the best area under the curve (AUC) was obtained for HOMA-R of 0.862. The HOMA-R 4.9 cut-off value had a 100% sensitivity (CI 95%: 96.2-100) and 67.9% specificity (CI 95%: 57.1-78.7) for severe steatosis. CONCLUSIONS: The presence of MetS and glucose metabolism alterations are risk factors for severe steatosis. The 4.9 cut-off value for HOMA-IR may be a risk factor for severe steatosis in obese children.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".