Fatness and Fitness in Obese Children at Low and High Health Risk
Bibliographic record
Abstract
We investigated whether body composition, physical activity, physical inactivity, and cardiorespiratory fitness explained the presence of risk factors for cardiovascular disease (CVD) and type 2 diabetes in youth. Eighty-three obese children (6–12 years old) were classified as either low health risk (LHR; n = 30) or high health risk (HHR; n = 53) based on the absence/presence of metabolic risk factors that included measures of dyslipidemia, insulin resistance, and elevated blood pressure. Along with demographic and anthropometric data, body composition, physical activity, physical inactivity, and cardiorespiratory fitness variables were assessed. Risk factor clustering was evident in this sample with 24/83 (29%) possessing at least 2 risk factors. Percent body fat did not differ between the LHR (38.5%) and HHR (39.8%) groups, but total fat mass, total fat-free mass, and central body fat mass were greater in the high health risk group. The strongest predictor for the presence of risk factors was central body fat accumulation. Physical activity, physical inactivity, and cardiorespiratory fitness were unable to predict metabolic risk. Overall, we found that risk factors for CVD and type 2 diabetes were common and that body fat mass and central body fat distribution, in particular, were more important than physical activity, physical inactivity, and cardiorespiratory fitness in predicting metabolic risk 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 | 0.001 | 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) | 0.001 | 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".