Muscle matters in diabetes: Indonesian adaptation of the sarcopenia geriatric scale and its links to glycemic control and fatigue
Bibliographic record
Abstract
Sarcopenia, marked by the progressive loss of muscle mass and function, is common in individuals with type 2 diabetes and is associated with fatigue, impaired glycemic control, and disability. However, culturally adapted screening tools for sarcopenia are scarce in Indonesia. This study aimed to adapt and validate the Indonesian version of the Sarcopenia Geriatric Scale (SARCO-GS-ID) and examine its associations with fatigue and glycemic control. A cross-sectional study was conducted among 125 adults with type 2 diabetes in two Indonesian outpatient clinics. The SARCO-GS was translated and culturally adapted following standard guidelines. Psychometric evaluations included content validity, exploratory factor analysis (EFA), internal consistency, test-retest reliability, and convergent and criterion validity. Multiple linear regression was performed to adjust for potential confounders. The SARCO-GS-ID demonstrated strong content validity (S-CVI = 0.91), internal consistency (Cronbach’s α = 0.82), and test-retest reliability (ICC = 0.89). EFA supported a two-factor structure reflecting subjective and objective domains, explaining 66.7% of the variance. Sarcopenia was present in 20.8% of participants and was associated with higher fatigue and poorer glycemic control (mean HbA1c = 8.7%) compared to non-sarcopenic individuals (HbA1c = 7.6%). However, these associations were attenuated when adjusted for BMI, comorbidities, and physical activity. The SARCO-GS-ID is a valid, reliable, and culturally appropriate tool for screening probable sarcopenia in Indonesian adults with type 2 diabetes. Its integration into routine care may support early detection and intervention, although confounding factors should be considered when interpreting associations with fatigue and metabolic outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".