Relationship between Nutritional Status, Frailty, and Cognitive Function among Elderly at Dr. H. Moch. Ansari Saleh General Hospital Banjarmasin
Bibliographic record
Abstract
The elderly often have nutritional problems. The risk of malnutrition can stimulate the prevalence of frailty and declining cognitive function in the elderly. The study aimed to analyze the relationship between nutritional status, frailty, and cognitive function in the elderly at Dr. H. Moch. Ansari Saleh Hospital Banjarmasin. This study was analytic observational using a cross-sectional method. A sample of 93 elderly was obtained using a total sampling technique according to the inclusion and exclusion criteria. The nutritional status assessment was done using the Mini Nutritional Assessment-Short Form (MNA-SF), the Edmonton Frailty Scale (EFS), and the Mini-Mental State Examination (MMSE). The results showed that the mean of Mini Nutritional Assessment score was 12.00±2.126, the average frailty score was 4.41±1.872, and the average value of cognitive function was 25.98±2.923. Data were analyzed using the Spearman's non-parametric correlation test with a 95% confidence level. The correlation test results between nutritional status and frailty obtained p=0.000 and r=-0.490. The correlation test results between nutritional status and cognitive function obtained p=0.000 and r=0.595. In short, there is a relationship between nutritional status on frailty and cognitive function in the elderly at Dr. H. Moch. Ansari Saleh General Hospital Banjarmasin, it means that the good nutritional status in the elderly, the risk of frailty syndrome will decrease and improve cognitive function.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".