Disentangling Cognitive-Frailty: Results From the Gait and Brain Study
Bibliographic record
Abstract
BACKGROUND: Cognitive-frailty, defined as the presence of both frailty and cognitive impairment, is proposed as a distinctive entity that predicts dementia. However, it remains controversial whether frailty alone, cognitive-frailty, or the combination of cognitive impairment and slow gait pose different risks of incident dementia. METHODS: Two hundred and fifty-two older adults free of dementia at baseline (mean age 76.6±8.6 years) were followed up to 5 years with bi-annual visits including medical, cognitive, and gait assessments. Incident all-cause of dementia and cognitive decline were the main outcomes. Frailty was defined using validated phenotypic criteria. Cognition was assessed using the Montreal Cognitive Assessment while gait was assessed using an electronic walkway. Cox Proportional Hazards models were used to estimate the risk of cognitive decline and dementia for frailty, cognitive-frailty, and gait and cognition models. RESULTS: Fifty-three participants experienced cognitive decline and 27 progressed to dementia (incident rate: 73/1,000 person-years). Frailty participants had a higher prevalence of cognitive impairment compared with those without frailty (77% vs. 54%, p = .02) but not significant risk to incident dementia. Cognitive-frailty increased incident rate (80/1,000 person-years) but not risk for progression to dementia. The combination of slow gait and cognitive impairment posed the highest risk for progression to dementia (hazard ratio: 35.9, 95% confidence interval: 4.0-319.2; p = 0.001, incident rate: 130/1,000 person-years). None of the models explored significantly predicted cognitive decline. CONCLUSIONS: Combining a simple motor test, such as gait velocity, with a reliable cognitive test like the Montreal Cognitive Assessment is superior than the cognitive-frailty construct to detect individuals at risk for dementia. Cognitive-frailty may embody two different manifestations, slow gait and low cognition, of a common underlying mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".