Utility of Childhood BMI in the Prediction of Adulthood Disease: Comparison of National and International References
Bibliographic record
Abstract
OBJECTIVE: To determine whether the U.S. Centers for Disease Control and Prevention (CDC; CDC Reference) or International Obesity Task Force (IOTF; IOTF Reference) BMI cut-off points for classifying adiposity status in children are more effective at predicting future health risk. RESEARCH METHODS AND PROCEDURES: The sample (N=1709) included 4- to 15-year-old (at baseline) boys and girls from the Bogalusa Heart Study. Overweight and obesity status were determined using both the CDC Reference and IOTF Reference BMI cut-off points at baseline. The ability of childhood overweight and obesity, determined from the two BMI classification systems, to predict obesity and metabolic disorders in young adulthood (after a 13- to 24-year follow-up) was then compared. RESULTS: Independently of the classification system employed to determine adiposity based on childhood BMI, the odds of being obese and having all of the metabolic disorders in young adulthood were significantly (p<0.05) higher in the overweight and obese groups by comparison with the nonoverweight groups. Childhood overweight and obesity, determined by both the CDC Reference and IOTF Reference, had a low sensitivity and a high specificity for predicting obesity and metabolic disorders in young adulthood. Overweight and obesity as determined by the CDC Reference were slightly more sensitive and slightly less specific than the corresponding values based on the IOTF Reference. DISCUSSION: Overweight and obesity during childhood, as determined by both the CDC and IOTF BMI cut-off points, are strong predictors of obesity and coronary heart disease risk factors in young adulthood. The differences in the predictive capacity of the CDC Reference and IOTF Reference are, however, 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".