Transitions in cognitive status in relation to frailty in older adults: A Comparison of three frailty measures
Bibliographic record
Abstract
OBJECTIVES: Cognitive decline is related to frailty. Frailty can be operationalized in different ways, which have an unknown impact on the estimation of risk. Here, we compared 3 frailty measures in relation to cognitive changes and mortality in the Canadian Study of Health and Aging (CSHA). DESIGN: Prospective population-based study, with 5 year follow up. PARTICIPANTS/SETTING: 2,305 subjects aged 70+ years. METHODS: For each participant, cognitive status was measured by the errors in the Modified Mini-Mental State Examination (3MS) score. Three frailty measures were used: a Frailty Index based on the Comprehensive Geriatric Assessment (FI-CGA) evaluated from 47 potential deficits, a Clinical Frailty Score and the Fried frailty phenotype. Multivariate Poisson regression and multivariate logistic regression were used to examine the association between baseline cognitive errors and frailty and death, respectively, while controlling for possible confounders (age, sex, education, and baseline cognitive status). RESULTS: Changes in cognitive status were strongly associated with baseline cognition and frailty, however defined. In multivariate models adjusted for age, sex and education, each frailty measure was associated with cognitive decline and with mortality. The frailest people (from the highest FI-CGA tertile) rarely showed cognitive improvement or stabilization (1.5%, 95% CI=0.002%-2.8%) compared with non-frail people (from the lowest tertile of the FI-CGA), of whom 27.8% (95% CI=24.5%-31.1%) did not deteriorate. CONCLUSIONS: Frail elderly people have an increased risk of cognitive decline. All frailty measures allowed quantification of individual vulnerability and predict both cognitive changes and mortality.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".