SARC-F predicts poor motor function, quality of life, and prognosis in older patients with cardiovascular disease and cognitive impairment
Bibliographic record
Abstract
OBJECTIVES: We investigated whether SARC-F scores were associated with motor function, quality of life (QOL) related to physical function, and prognosis in older patients with cardiovascular disease (CVD) and cognitive impairment. METHODS: This was a retrospective cross-sectional cohort study. The study population consisted of 408 patients with CVD (≥60 years old) who completed the SARC-F questionnaire and Mini-Cog, a cognitive function test, at discharge. Sarcopenia was defined as a total SARC-F score ≥ 4 points. Patients who were cognitively-preserved (Mini-Cog score ≥ 3 points) were excluded. Patients completed the handgrip strength, leg strength, usual gait speed, 6-minute walking distance, short physical performance battery score, and 36-item Short-Form Health Survey Physical Functioning (SF-36PF) tests before discharge. Associations of SARC-F with physical function, QOL, and prognoses (i.e., composite of all-cause death and emergency CVD rehospitalization and the number of CVD rehospitalizations) were investigated. RESULTS: Sarcopenia (SARC-F score ≥ 4 points) was associated with poorer motor function test outcomes and SF-36PF scores (all P < 0.001). The correlations remained significant after adjusting for comorbidities (e.g., anemia, prior heart failure, and renal dysfunction). Sarcopenia was also associated with a poorer prognosis (hazard ratio: 1.574; 95 % confidence interval [CI], 1.011-2.445) and an increased risk of CVD rehospitalization (incidence rate ratio: 1.911; 95 % CI, 1.312-2.782) after adjusting for comorbidities. CONCLUSIONS AND IMPLICATIONS: In older patients with CVD and cognitive impairment, the SARC-F questionnaire may be a simple and inexpensive tool for identifying patients with decreased motor function and a poor prognosis.
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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 | 0.001 | 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 | 0.001 |
| 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".