Epidemiology of Sarcopenia
Bibliographic record
Abstract
OBJECTIVES: To examine patterns of muscle mass change with aging and to estimate the prevalence of sarcopenia. DESIGN: Cross-sectional survey. SETTING: Population-based study in Rochester, Minnesota. PARTICIPANTS: Age-stratified sample of men and women from the community. MEASUREMENTS: Muscle mass estimated from total body scans by dual-energy X-ray absorptiometry. Sarcopenia was defined as muscle mass more than 2 standard deviations below the sex-specific young-normal mean. RESULTS: Total lean body mass (exclusive of bone) and total skeletal muscle mass both were greater in men than women and declined linearly with age as judged from these cross-sectional data. Adjustment for height reduced the gender difference. The age- and sex-adjusted prevalence of sarcopenia varied from 6 to 15% among subjects 65 years of age or over, depending on the muscle mass parameter that was evaluated, but prevalence rates were quite sensitive to the normal values used to define cutoff levels. Subjects with sarcopenia appeared to have more physical limitations than the others. CONCLUSIONS: Late in life, a substantial portion of the population reaches low levels of muscle mass that are associated with increased physical disability. However, additional efforts are needed to validate an operational definition of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".