Arabic Adaptation and Validation of the SARC‐F Questionnaire for Sarcopenia Screening in Elderly Populations: Exploration of Associated Factors
Bibliographic record
Abstract
Background: Sarcopenia is a geriatric condition marked by decreased muscle mass and function as people age. The SARC‐F questionnaire is a simple and useful instrument for sarcopenia screening but it is not available in the Arabic language. This study aimed to translate the SARC‐F into the Arabic language, validate it among Arabic‐speaking older adults, and explore the association between SARC‐F and fatigue, QOL, and cognitive impairment. Methods: SARC‐F was translated into Arabic according to WHO guidelines, and older adults aged 60 years and older were recruited. Test–retest reliability of SARC‐F was examined over a 2‐week period. SARC‐F was validated based on the revised European Working Group on Sarcopenia in Older People criteria. Sensitivity, specificity, and positive and negative predictive values were assessed against bioelectrical impedance analysis (BIA). The association between the Arabic SARC‐F and Modified Fatigue Impact Scale, Medical Outcomes Study Short Form 12, and Montreal Cognitive Assessment was investigated too. Results: Eighty‐six older adults participated in this study (59.3% females). The Arabic SARC‐F showed an intraclass correlation coefficient (ICC) of 0.926 (95% CI = 0.88–0.95) and Cronbach’s alpha of 0.81. Sensitivity, specificity, and positive predictive value and negative predictive value were 36.4%, 78.7%, 20%, and 89.3%, respectively. The Arabic version of SARC‐F showed good reliability and validity. Conclusion: The Arabic SARC‐F is a valid and reliable tool for sarcopenia screening, showing a good ability to identify individuals with sarcopenia and predict the absence of the condition. The Arabic SARC‐F was associated with fatigue and QOL but not with cognitive impairment. These results support the use of the Arabic SARC‐F as a useful questionnaire for sarcopenia screening in Arabic‐speaking populations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".