Prevalence and risk factors of cognitive frailty among pre‐frail and frail older adults in nursing homes
Bibliographic record
Abstract
BACKGROUND: The purpose of this research was to stratify the level of frailty to examine the risk factors associated with reversible cognitive frailty (RCF) and potentially reversible cognitive frailty (PRCF) in nursing homes to provide a basis for hierarchical management in different stages of frailty. METHODS: The study was a cross-sectional study conducted from September to November 2022; 504 people were selected by stratified random sampling after convenience selection from the Home for the Aged Guangzhou. The structured questionnaire survey was conducted through face-to-face interviews using the general data questionnaire, Fried Frailty Phenotype, Montreal Cognitive Assessment Scale. RESULTS: In total, 452 individuals were included for analysis. A total of 229 cases (50.7%) were PRCF, 70 (15.5%) were RCF. Multivariate logistic regression analysis showed that in pre-frailty, the Geriatric Depression Scale (GDS-15) score (odds ratio (OR) 1.802; 95% CI 1.308-2.483), Instrumental Activities of Daily Living Scale (IADL) score (0.352; 0.135-0.918) and energy (0.288; 0.110-0.755) were influencing factors of RCF. GDS-15 score (1.805; 1.320-2.468), IADL score (0.268; 0.105-0.682), energy (0.377; 0.150-0.947), lack of intellectual activity (6.118; 1.067-35.070), admission time(>3 years) (9.969; 1.893-52.495) and low education (3.465; 1.211-9.912) were influencing factors of PRCF. However, RCF with frailty was associated with the Short-Form Mini-Nutritional Assessment (MNA-SF) score (0.301; 0.123-0.739) and low education time (0 ~ 12 years) (0.021; 0.001-0.826). PRCF with frailty was associated with age (1.327; 1.081-1.629) and weekly exercise time (0.987; 0.979-0.995). CONCLUSIONS: The prevalence of RCF and PRCF was high among pre-frail and frail older adults in nursing homes. Different levels of frailty had different influencing factors for RCF and PRCF. Depression, daily living ability, energy, intellectual activity, admission time, education level, nutrition status, age and exercise time were associated with RCF and PRCF. Hierarchical management and intervention should be implemented for different stages of frailty to prevent or delay the progression of cognitive frailty.
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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 | 0.001 |
| 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".