Assessment of SARC-F Sensitivity for Probable Sarcopenia Among Community-Dwelling Older Adults: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: The European Working Group on Sarcopenia in Older People (EWGSOP2) recommends the use of the 5-item SARC-F (strength, assistance with walking, rising from a chair, climbing stairs, and falls) questionnaire by clinicians to screen for probable sarcopenia. The recommended threshold of ≥4 has low sensitivity and high specificity in identifying probable sarcopenia. While this high threshold is effective in excluding clients without probable sarcopenia, challenges exist in using this screening tool to identify clients with low muscle strength. Objective: This study aims to reassess the use of SARC-F in a primary care clinic for the determination of incidence of probable sarcopenia and to evaluate if a handgrip strength test is necessary for its diagnosis. Methods: We screened 204 patients aged ≥65 years (117 men and 87 women) during routine visits with the SARC-F questionnaire. Probable sarcopenia was defined by EWGSOP2 grip strength cut points (≤27 kg for men and ≤16 kg for women). Receiver operating characteristic analysis was performed to identify the SARC-F threshold that best balanced sensitivity and specificity. Results: Probable sarcopenia was present in 12% (n=24) of participants. The mean age (73.9, SD 6.2 years) and mean BMI (29.5, SD 5.8 kg/m²) did not differ significantly by sex; however, men showed a higher mean grip strength (36.3, SD 8.1 kg vs 22.4, SD 5.5 kg; P<.001) and lower mean SARC-F scores (0.9, SD 1.7 vs 1.9, SD 2.3; P<.001). A SARC-F cut point of ≥2 yielded an area under the curve of 0.77 (95% CI 0.67-0.88), with sensitivity of 0.78, specificity of 0.75, accuracy of 0.77, positive predictive value of 0.31, and negative predictive value of 0.96. The grip strength differed significantly between screen-positive and screen-negative groups at both the ≥2 and ≥4 thresholds (P<.001). Conclusions: A SARC- F threshold of ≥2 is recommended as an optimal trade-off between sensitivity and specificity for identifying community-dwelling older adults with probable sarcopenia. This threshold is lower than the currently accepted recommendation of ≥4. Our findings promote the recommendations for early detection and treatment by medical professionals following the EWGSOP2 by improving the ability of clinicians to identify individuals with low muscle strength using this screening procedure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".