Cognitive Status With Frailty Scale In The Elderly People At Santo Yosef Nursing Home Surabaya
Bibliographic record
Abstract
Introduction: The increasing life expectancy of the elderly in Indonesia is also followed by an increase in morbidity rate and decreasing the elderly’s physiological function of the body. One of the health problems is decreasing cognitive function. A pathological result of decreasing cognitive function causes frailty in the elderly. Aim : The purpose of this research is to analyze the correlation between cognitive status with frailty scale in the elderly at Santo Yosef Nursing Home Surabaya in 2017. Method : The type of this research is observational analytic with a cross-sectional study and purposive sampling technique. The instrument that been used in this research is the Montreal Cognitive Assessment (MoCA) Indonesian version (MoCa-Ina) and FRAIL Scale. Data collected through the interview method. Result : We used the Rank Spearman correlation test as the analytic test; the result suggesting that there is a significant correlation (p=0,000) with moderate strength of correlation (r=0,593) between cognitive status with the Frailty Scale. Conclusion : From the result, we can conclude that there is a correlation between cognitive status with the Frailty Scale in the elderly
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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 | 0.001 |
| 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".