Accuracy of Body Fat Cutoff Points in Predicting Fall Risk Among Older Women
Bibliographic record
Abstract
Introduction Physiological changes occur with aging, including a reduction in body weight and lean mass, along with an increase in body fat. Overweight and/or obese older adults have an increased risk of falls.Aim To identify the cutoff points for fall risk in older women based on body fat mass and fat percentage.Method This cross-sectional study included 182 older women (age: 70 ± 6 for the non-fallers group and 68 ± 6 for the fallers group). Body weight and height were measured, and body composition analysis was performed using bioelectrical impedance analysis. The receiver operating characteristic (ROC) curve was used to determine cutoff values.Results The cutoff point for fat mass was 25 kg, and for fat percentage, it was 35%, with an area under the curve (AUC) of 0.71 and 0.72, respectively.Conclusion The cutoff points of 25 kg for body fat mass and 35% for fat percentage may serve as references for identifying older women at an increased risk of falls.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".