Frailty, sarcopenia and health related outcomes among elderly patients in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of frailty and sarcopenia among elderly patients in Saudi Arabia and explore if there are significant association between frailty and sarcopenia. METHODS: A total of 498 patients from public tertiary hospital in Saudi Arabia participated in this descriptive cross-sectional study between March 2019 to June 2019. All participants answered a 5-part questionnaire, which includes demographic data, Edmonton Frail Scale, SARC-F and questions related to Activities of Daily living. RESULTS: . The prevalence of patients with mild frail, moderate frail and severely frail were 22, 12, and 4%, respectively. The analysis showed that majority of patients who had sarcopenia were females (84%). The analysis show that the level of frailty of patients were significantly different between age, marital status, educational level and patients' needs of home care, activities of daily living, presence of comorbidity and sarcopenia (p = 0.001). In the logistic regression analysis, the pre-frailty group was significantly likely to have sarcopenia (OR 0.02 95% 0.01-0.23p = 0.001) than nonfrailty patients. CONCLUSION: In conclusion, this research highlights the high prevalence of sarcopenia among elderly patients and the increasing percentage of frail patients in Saudi Arabia. In addition, significant difference and association were found with sarcopenia and frailty with many sociodemographic and clinical components of elderly patients in Saudi Arabia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".