Is BMI a valid measure of obesity in postmenopausal women?
Bibliographic record
Abstract
OBJECTIVE: Body mass index (BMI) is a widely used indicator of obesity status in clinical settings and population health research. However, there are concerns about the validity of BMI as a measure of obesity in postmenopausal women. Unlike BMI, which is an indirect measure of obesity and does not distinguish lean from fat mass, dual-energy x-ray absorptiometry (DXA) provides a direct measure of body fat and is considered a gold standard of adiposity measurement. The goal of this study is to examine the validity of using BMI to identify obesity in postmenopausal women relative to total body fat percent measured by DXA scan. METHODS: Data from 1,329 postmenopausal women participating in the Buffalo OsteoPerio Study were used in this analysis. At baseline, women ranged in age from 53 to 85 years. Obesity was defined as BMI ≥ 30 kg/m and body fat percent (BF%) greater than 35%, 38%, or 40%. We calculated sensitivity, specificity, positive predictive value, and negative predictive value to evaluate the validity of BMI-defined obesity relative BF%. We further explored the validity of BMI relative to BF% using graphical tools, such as scatterplots and receiver-operating characteristic curves. Youden's J index was used to determine the empirical optimal BMI cut-point for each level of BF% defined obesity. RESULTS: The sensitivity of BMI-defined obesity was 32.4% for 35% body fat, 44.6% for 38% body fat, and 55.2% for 40% body fat. Corresponding specificity values were 99.3%, 97.1%, and 94.6%, respectively. The empirical optimal BMI cut-point to define obesity is 24.9 kg/m for 35% BF, 26.49 kg/m for 38% BF, and 27.05 kg/m for 40% BF according to the Youden's index. CONCLUSIONS: Results demonstrate that a BMI cut-point of 30 kg/m does not appear to be an appropriate indicator of true obesity status in postmenopausal women. Empirical estimates of the validity of BMI from this study may be used by other investigators to account for BMI-related misclassification in older women.
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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 | 0.011 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".