A rapid point-of-care ultrasound marker for muscle mass and muscle strength in older adults
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Sarcopenia is defined as the gradual age-associated loss of both muscle quantity and strength in older adults, and is associated with increased mortality, falls, fractures and hospitalisations. Current sarcopenia criteria use dual-energy X-ray absorptiometry (DXA) measures of muscle mass, a test that cannot be performed at the bedside, unlike point-of-care ultrasound (PoCUS). We examined the association between ultrasonic measures of muscle thickness (MT, vastus medialis muscle thickness) and measures of muscle quantity and strength in older adults. METHODS: A total of 150 older adults (age ≥ 65; mean age 80.0 ± 0.5 years, 66 women, 84 men) were recruited sequentially from geriatric medicine clinics. Each subject had lean body mass (LBM, by bioimpedance assay), grip strength, mid-arm biceps circumference (MABC), gait speed and MT measured. All initial models were adjusted for biological sex. RESULTS: In our final parsimonious models, MT showed a strong significant correlation with all measures of muscle mass, including LBM (Standardised β = 0.204 ± 0.058, R2 = 0.577, P < 0.001) and MABC (Standardised β = 0.141 ± 0.067, R2 = 0.417, P = 0.038). With respect to measures of muscle quality, there was a strong significant correlation with grip strength (Standardised β = 0.118 ± 0.115, R2 = 0.511, P < 0.001) but not with subject performance (gait speed). CONCLUSIONS: MT showed strong correlations with both measures of muscle mass (LBM and MABC) and with muscle strength (grip strength). Although more work needs to be done, PoCUS shows potential as a screening tool for sarcopenia in older adults.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".