Development and Validation of a Tool to Screen for Cognitive Frailty Among Community-Dwelling Elders
Bibliographic record
Abstract
OBJECTIVES: Reciprocal age-related impairments in physical and cognitive functioning have been termed 'cognitive frailty', which is associated with adverse health outcomes and is a potential target for preventing or delaying the onset of disability in older people. However, cognitive frailty as currently defined is challenging to diagnose. To facilitate earlier diagnosis and intervention, we conducted this study to develop and validate a simple evidence-based instrument to identify community-dwelling elders at risk of cognitive frailty. DESIGN: Retrospective analyses of data from the I-Lan Longitudinal Aging Study (ILAS) to develop a prediction model, and from the Longitudinal Aging Study of Taipei (LAST) for external validation. SETTING: Community-dwelling adults from Taipei City, New Taipei City and Yilan (I-Lan) County, Taiwan. PARTICIPANTS: 1271 community residents ≥65 years old, without impaired global cognition or dependency for activities of daily living/instrumental activities of daily living. MEASUREMENTS: Demographic characteristics, anthropometric measurements, medical history, Mini-Mental State Examination, Montreal Cognitive Assessment, Functional Autonomy Measuring System, Functional Assessment Staging Test, Center for Epidemiologic Studies Depression Scale, handgrip strength, 6-metre walk speed. METHODS: Baseline characteristics of groups with/without cognitive frailty were analyzed and factors differing significantly in univariate analysis input to binary logistic regression to develop a cognitive frailty risk (CFR) score. RESULTS: The prevalence of cognitive frailty was 15.8% overall; ILAS 21.4%, LAST 8.4%. Predictors of CFR comprised: age ≥75 years; female sex; waist circumference ≥90 cm (male), ≥80 cm (female); calf circumference <33 cm (male), <32 cm (female); memory deficits; and diabetes mellitus. CFR ≥5/14 had sensitivity of 70%, specificity of 60%, and predictive accuracy of 72%. CONCLUSIONS: A CFR score based on simple history-taking and anthropometric measurements integrates age, sex, cardiometabolic risk, memory deficits, sarcopenia, and nutrition, with validated predictive accuracy, and could be performed easily in community settings to identify seniors with cognitive frailty for appropriate interventions.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".