Association of Sonographic Sarcopenia and Falls in Older Adults Presenting to the Emergency Department
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To determine the association between point-of-care-ultrasonography (POCUS)-measured sarcopenia and grip strength, as well as the history of prior-year falls among older adults admitted to the emergency department observation unit (EDOU). MATERIALS AND METHODS: This cross-sectional observational study was conducted over 8 months at a large urban teaching hospital. A consecutive sample of patients who were 65 years or older and admitted to the EDOU were enrolled in the study. Using standardized techniques, trained research assistants and co-investigators measured patients' biceps brachii and thigh quadriceps muscles via a linear transducer. Grip strength was measured using a Jamar Hydraulic Hand Dynamometer. Participants were surveyed regarding their history of falls in the prior year. Logistic regression analyses assessed the relationship of sarcopenia and grip strength to a history of falls (the primary outcome). RESULTS: Among 199 participants (55% female), 46% reported falling in the prior year. The median biceps thickness was 2.22 cm with an Interquartile range [IQR] of 1.87-2.74, and the median thigh muscle thickness was 2.91 cm with an IQR of 2.40-3.49. A univariate logistic regression analysis demonstrated a correlation between higher thigh muscle thickness, normal grip strength, and history of prior-year falling, with an odds ratio [OR] of 0.67 (95% conference interval [95%CI] 0.47-0.95) and an OR of 0.51 (95%CI 0.29-0.91), respectively. In multivariate logistic regression, only higher thigh muscle thickness was correlated with a history of prior-year falls, with an OR of 0.59 (95% CI 0.38-0.91). CONCLUSIONS: POCUS-measured thigh muscle thickness has the potential to identify patients who have fallen and thus are at high risk for future 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 | 0.000 | 0.004 |
| 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 | 0.000 | 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".