Relationship Between Vitamin D Deficiency And Insulin Resistance In Obese Children
Bibliographic record
Abstract
INTRODUCTION: Obesity represents the major risk factor for development of insulin resistance during childhood and adolescents. In obesity, adipose tissue release free fatty acids, various hormones, and cytokines, resulting in insulin resistance. This study aimed to establish the correlation between vitamin D deficiency and the incidence of insulin resistance in obese children. DESIGN AND METHOD: This analytical cross-sectional study was arranged from December 2019 - February 2020 included 96 students aged 11 - 17 years old from junior and senior high school who met the criteria for obesity in Makassar. The study subjects were parted into two groups, obese children with vitamin D deficiency (levels of 25-hydroxyvitamin D ≤ 20 ng/ml) and obese children without vitamin D deficiency group (levels of 25-hydroxyvitamin D > 20 ng/ml). Data were analyzed using univariate and bivariate analysis. RESULTS: The frequency of insulin resistance in obese children with vitamin D deficiency was 28 (54.9%), while obese children without vitamin D deficiency was 10 (22.2%). Based on statistical analysis, the frequency of the occurrence of insulin resistance in vitamin D deficiency obese children was higher than in obese children without vitamin D deficiency with OR = 4.261 (95% CI 1.744 – 10.411), p = 0.001. CONCLUSION: The risk of insulin resistance in obese children with vitamin D deficiency is 4.261 times higher than obese children without vitamin D deficiency.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".