Skeletal Muscle Cutpoints Associated with Elevated Physical Disability Risk in Older Men and Women
Bibliographic record
Abstract
The purpose of this study was to determine skeletal muscle cutpoints for identifying elevated physical disability risk in older adults. Subjects included 4,449 older (> or = 60 years) participants from the Third National Health and Nutrition Examination Survey during 1988-1994. Physical disability was assessed by questionnaire, and bioimpedance was used to estimate skeletal muscle, which was normalized for height. Receiver operating characteristics were used to develop the skeletal muscle cutpoints associated with a high likelihood of physical disability. Odds for physical disability were compared in subjects whose measures fell above and below these cutpoints. Skeletal muscle cutpoints of 5.76-6.75 and < or =5.75 kg/m2 were selected to denote moderate and high physical disability risk in women. The corresponding values in men were 8.51-10.75 and < or =8.50 kg/m2. Compared with women with low-risk skeletal muscle values, women with moderate- and high-risk skeletal muscle values had odds for physical disability of 1.41 (95% confidence interval (CI): 0.97, 2.04) and 3.31 (95% CI: 1.91, 5.73), respectively. The corresponding odds in men were 3.65 (95% CI: 1.92, 6.94) and 4.71 (95% CI: 2.28, 9.74). This study presents skeletal muscle cutpoints for physical disability risk in older adults. Future applications of these cutpoints include the comparison of morbidity risk in older persons with normal muscle mass and those with sarcopenia, the determination and comparison of sarcopenia prevalences, and the estimation of health-care costs attributable to sarcopenia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".