Screening for sarcopenia with SARC-F in older patients hospitalized with cardiovascular disease
Bibliographic record
Abstract
AIMS: SARC-F ≥ 4 points are used for detecting sarcopenia; however, finding a lower SARC-F cut-off value may lead to early detection of sarcopenia. We investigated the SARC-F score with the highest sensitivity and specificity values to identify sarcopenia in older patients with cardiovascular disease (CVD). Motor performances were also examined for each SARC-F score. METHODS AND RESULTS: This retrospective cross-sectional study examined the sensitivity and specificity of every 1-point increase in the SARC-F score to predict sarcopenia. Eligible participants included patients with CVD (≥65 years old) who were admitted for acute CVD treatment and participated in cardiac rehabilitation. Patients completed the SARC-F questionnaire and the sarcopenia assessment. Area under the curves (AUCs) were investigated for the ability to predict sarcopenia. Multivariable linear regression was used to compare the mean value of physical functions (e.g. walking speed, leg strength, and 6 min walking distance) of each SARC-F score. A total of 1066 participants (63.8% male; median age: 76 years) were included. Sarcopenia was present in 401 patients. A SARC-F cut-off ≥2 presented the optimal balance between sensitivity (68.3%) and specificity (55.6%) to detect sarcopenia (AUCs = 0.658; 95% confidence interval: 0.625-0.691). When the patients had low scores (1-3), every 1 point increase in the SARC-F score was associated with lower physical functions such as lower muscle strength and shorter walking distance (all P < 0.001). CONCLUSION: A SARC-F cut-off ≥2 was optimal for screening sarcopenia, and even a low SARC-F score is useful in detecting sarcopenia and low physical function at an early stage in patients with CVD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".