Associations Between Frailty, Sarcopenia, and Nutritional Status in Older Adults Living in Nursing Homes
Bibliographic record
Abstract
Background/Objectives: Increasing awareness of factors that put the population at high risk of frailty is essential to prevent frailty and minimize its adverse consequences. Methods: In this cross-sectional study, participants were over the age of 65 and living in nursing homes. The Edmonton Frailty Scale was used to determine frailty, the Sarcopenia Rapid Screening Test (SARC-F) was used to assess sarcopenia, and the Mini Nutritional Assessment (MNA) questionnaire and 7-day 24-h dietary recall were used to determine the nutritional status of the older adult population. Data were analyzed by SPSS 25.0 for Windows (Statistical Package for Social Sciences). Results: The frailty scale score of gender was statistically significant (p < 0.05). There was a statistically significant difference in sarcopenia status and malnutrition based on the distribution of the frailty status among the participants (p < 0.05). There was a statistically significant difference in vitamin C intake adequacy according to the distribution of frailty status among older adults (p < 0.05). There was a positive correlation between frailty status and sarcopenia (r = 0.773; p < 0.05). Frailty and nutritional status were significantly negatively correlated (r = −0.496; p < 0.05). There was a significant positive correlation between the sarcopenia status and malnutrition status of the participants (r = 0.489; p < 0.005). Conclusions: Older adults living in nursing homes are at risk for frailty syndrome, malnutrition, and sarcopenia. Evaluating older adults in terms of all these factors and implementing daily nutrition plans and support according to these results is of great importance for promoting a healthy life.
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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 | 0.001 | 0.003 |
| 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".