Determining ethnic-, gender-, and age-specific waist circumference cut-off points to predict metabolic syndrome: the Sympathetic Activity and Ambulatory Blood Pressure in Africans (SABPA) study
Bibliographic record
Abstract
Objective: The aim was to determine receiver-operating characteristic (ROC) waist circumference (WC) cut-off points best associated with metabolic syndrome (MetS) in a cohort of South African teachers.Design: Target population study.Setting and subjects: Four hundred and nine urban black (Africans) and white (Caucasians) from the Kenneth Kaunda district in North West province, between the ages of 25 and 65 years old, were stratified according to gender and age (25–45 years and 46–65 years).Outcome measures: Anthropometric, fasting overnight urine and biological markers for MetS.Results: ROC analysis determined pathological WC cut-off points of 91 cm for African men and 84 cm for African women. It is recommended that WC cut-off points should be 97 cm for Caucasian men and 84 cm for Caucasian women. Pathological WC cut-off points significantly predicted MetS in all ethnic-, gender- and age- specific groups, especially in male groups, with odds ratios of 7.6 [95% confidence interval (CI): 3.4–17.1, p-value = 0.00) for African men and 6 (95% CI: 3–12.1, p-value = 0.00) for Caucasian men.Conclusion: ROC-developed WC cut-off points were found to be good predictors of MetS in a South African cohort, especially in the men. Further research in prospective cohort studies is warranted to verify our findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".