Body Mass Index, Waist Circumference, and Clustering of Cardiovascular Disease Risk Factors in a Biracial Sample of Children and Adolescents
Bibliographic record
Abstract
OBJECTIVE: To derive optimal body mass index (BMI) and waist circumference thresholds for children and adolescents, to predict risk factor clustering. DESIGN: Cross-sectional receiver operating characteristic curve analysis. SETTING: The Bogalusa Heart Study, a community-based study of cardiovascular disease risk factors in early life. PARTICIPANTS: A total of 2597 black and white children and adolescents, 5 to 18 years of age, who were examined between 1992 and 1994. MAIN OUTCOME MEASURES: The presence or absence of > or =3 age-adjusted risk factors (low high-density lipoprotein cholesterol level, high low-density lipoprotein cholesterol level, high triglyceride level, high glucose level, high insulin level, and high blood pressure) was predicted from age-adjusted BMI and waist circumference values. RESULTS: The areas under the receiver operating characteristic curves were significantly different from 0.5 for both BMI and waist circumference for all gender/race groups, ranging from 0.73 to 0.82. The optimal BMI thresholds were at the 53rd and 50th percentiles for white and black male subjects, respectively, and at the 57th and 51st percentiles for white and black female subjects, respectively. Similarly, the optimal waist circumference thresholds were at the 56th and 50th percentiles for white and black male subjects, respectively, and at the 57th and 52nd percentiles for white and black female subjects, respectively. The sensitivity and specificity at the thresholds were similar for all gender/race groups, ranging from 67% to 75%. CONCLUSIONS: The use of BMI and waist circumference for the prediction of risk factor clustering among children and adolescents has significant clinical utility. In this sample, race and gender differences in the optimal thresholds were minimal.
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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 | 0.003 |
| 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) | 0.001 | 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".