Predicting abdominal adipose tissue in overweight Latino youth
Bibliographic record
Abstract
OBJECTIVES: 1) Examine associations between visceral adipose tissue (VAT), subcutaneous abdominal adipose tissue (SAAT), and anthropometric and demographic variables; 2) generate and cross-validate prediction equations for estimating VAT and SAAT in overweight Latino children. STUDY DESIGN: Cross-sectional. PARTICIPANTS: 196 overweight 8-13-year-old Latino youth. Two-thirds (n = 131) were randomly assigned to a development group to generate prediction equations for VAT and SAAT; one-third (n = 65) was used as a cross-validation group. METHODS AND PROCEDURES: Anthropometric measurements (height, weight, skinfold thicknesses, and circumferences) were performed. VAT and SAAT were measured using magnetic resonance imaging (MRI). RESULTS: The strongest univariate correlate for VAT was waist circumference (WC) (r = 0.65, p < 0.01) while the strongest correlate for SAAT was hip circumference (r = 0.88, p < 0.001). Regression analyses showed approximately 50% of the variance in VAT was explained by WC (43.8%), Tanner stage (4.2%) and calf skinfold (1.7%). Variance in the SAAT model was explained by WC (77.8%), triceps skinfold (4.2%) and gender (2.3%). Residual analyses showed no bias in either equation. Though mean differences between measured and predicted VAT and SAAT were small, there was a large degree of variability at the individual level especially for VAT. CONCLUSIONS: Both VAT and SAAT prediction equations performed well at the group level, but the relatively high degree of variability suggests limited clinical utility of the VAT equation. MRI is currently required to derive an accurate measure of VAT at the individual level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".