BMI-specific waist circumference is better than skinfolds for health-risk determination in the general population
Bibliographic record
Abstract
Distribution of fat is important when considering health risk; however, the value added from skinfold measurements (SKF) when using body mass index (BMI) refined by waist circumference (WC) is not well understood. The purpose of this study was to assess the utility of SKF compared with WC in determination of health risk in the general population. Data from the Canadian Health Measures Survey (cycles 1 and 2; N = 5217) were used. Health outcomes included directly measured blood pressure, cholesterol, glycated haemoglobin, lung function, self-reported health, and chronic conditions. Technical errors of measurements (TEM), sensitivity, and specificity analysis and linear regressions were conducted. Data indicated that TEM for SKF was above the acceptable 5% in most age and sex categories. Sensitivity and specificity of chronic conditions was not improved with the inclusion of SKF in models containing WC (in those aged 45-69 years) and SKF did not explain any additional variance in regression models containing WC. Health outcomes for those in the normal weight and overweight BMI category were significantly worse in those classified as high risk based on WC, whereas SKF did not consistently discriminate risk. In conclusion, evidence-based WC cut-points were shown to identify health risk, particularly in normal weight and overweight individuals. Thus, BMI refined by WC appears to be more appropriate than SKF for assessment of body composition when determining health risk in the general population.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".