Frailty, Cognitive Impairment, and Incident Major Neurocognitive Disorders: Results of the NuAge Cohort Study
Bibliographic record
Abstract
BACKGROUND: Frailty is associated with an increased risk of major neurocognitive disorders (MNCD). OBJECTIVE: This study aims to compare the Fried physical model and the CARE deficit accumulation model for their association with incident major neurocognitive disorders (MNCD), and to examine how the addition of cognitive impairment to these frailty models impacts the incidence in community-dwelling older adults. METHODS: A subset of community dwellers (n = 1,259) who participated in the "Quebec Longitudinal Study on Nutrition and Successful Aging" (NuAge) were selected in this Elderly population-based observational cohort study with 3 years of follow-up. Fried and CARE frailty stratifications into robust, pre-frail and frail groups were performed using the NuAge baseline assessment. Incident MNCD (i.e., Modified Mini Mental State (3MS) score < 79/100 and Instrumental Activity Daily Living (IADL) score < 6/8) were collected each year over a 3-year follow-up period. RESULTS: A greater association with incident MNCD of the CARE frail state was observed with an increased predictive value when combined with cognitive impairment in comparison to Fried's one, the highest incidences being observed using the robust state as the reference. Results with the Fried frail state were more heterogenous, with no association with the frail state alone, whereas cognitive impairment alone showed the highest significant incidence. CONCLUSION: The association of the CARE frail state with cognitive impairment increased the predictive value of MNCD, suggesting that the CARE frailty model may be of clinical interest when screening MCND in the elderly population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".