Improving the Identification of Frailty in Long-Term Care Residents: A Cross-Sectional Study
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Bibliographic record
Abstract
PURPOSE: To compare the capacity of blood myostatin concentration and physical, cognitive, and affective function tests to predict frailty among long-term care (LTC) residents. METHODS: This cross-sectional analysis used baseline data from three randomized controlled trials involving 260 older adults in 14 LTC centers. Serum myostatin levels were analyzed by enzyme-linked immunosorbent assay. Frailty, physical fitness, cognitive and affective functions were assessed using validated tests and scales. RESULTS: The Timed Up and Go, gait speed, 6-minute walk, and Berg Balance Scale had excellent capabilities in identifying frail individuals in accordance with Fried's Frailty Phenotype (FFP). The best tests for identifying frailty in accordance with the Clinical Frailty Scale (CFS) were Timed Up and Go and Berg Balance Scale. For the Tilburg Frailty Indicator (TFI), the best tests were Quality of Life in Alzheimer's Disease (QoL-AD) and Goldberg Anxiety. Myostatin, along with physical, cognitive, and affective function tests, improved the capability of the hand grip, arm-curl, Montreal Cognitive Assessment, Goldberg Anxiety, Goldberg Depression, and QoL-AD to identify frailty according to FFP, while myostatin improved CFS-defined frailty identification by the hand grip, arm-curl, 6-minute walk test, Berg Balance Scale, 30-second chair-stand, gait speed, Montreal Cognitive Assessment, Goldberg Anxiety, and De Jong-Gierveld Loneliness Scale. CONCLUSION: Among LTC residents, serum myostatin was associated with being frail according to FFP and CFS. However, this measure was less discriminating of frailty than physical fitness tests (for FFP and CFS) and affective function parameters (for TFI). However, evaluated concurrently with physical, cognitive, and affective parameters, myostatin improved the capabilities of these measures to predict CFS-defined frailty.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it