Development of Health‐Related Waist Circumference Thresholds Within BMI Categories
Bibliographic record
Abstract
OBJECTIVE: To develop and cross-validate waist circumference (WC) thresholds within BMI categories. The utility of the derived values was compared with the single WC thresholds (women, 88 cm; men, 102 cm) recommended by NIH and Health Canada. RESEARCH METHODS AND PROCEDURES: The sample included adults classified as normal weight (BMI = 18.5 to 24.9), overweight (BMI = 25 to 29.9), obese I (BMI = 30 to 34.9), and obese II+ (BMI > or = 35) from the Third U.S. National Health and Nutrition Examination Survey (NHANES III; n = 11,968) and the Canadian Heart Health Surveys (CHHS; n = 6286). Receiver operating characteristic curves were used to determine the optimal WC thresholds that predicted high risk of coronary events (top quintile of Framingham scores) within BMI categories using the NHANES III. The BMI-specific WC thresholds were cross-validated using the CHHS. RESULTS: The optimal WC thresholds increased across BMI categories from 87 to 124 cm in men and from 79 to 115 cm in women. The validation study indicated improved sensitivity and specificity with the BMI-specific WC thresholds compared with the single thresholds. DISCUSSION: Compared with the recommended WC thresholds, the BMI-specific values improved the identification of health risk. In normal weight, overweight, obese I, and obese II+ patients, WC cut-offs of 90, 100, 110, and 125 cm in men and 80, 90, 105, and 115 cm in women, respectively, can be used to identify those at increased risk.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".