CFAI‐Plus: Adding cognitive frailty as a new domain to the comprehensive frailty assessment instrument
Bibliographic record
Abstract
OBJECTIVES: Cognitive frailty is characterized by the presence of cognitive impairment in exclusion of dementia. In line with other frailty domains, cognitive frailty is associated with negative outcomes. The Comprehensive Frailty Assessment Instrument (CFAI) measures 4 domains of frailty, namely physical, psychological, social, and environmental frailty. The absence of cognitive frailty is a limitation. METHOD: An expert panel selected 6 questions from the Informant Questionnaire on Cognitive Decline that were, together with the CFAI and the Montreal cognitive assessment administered to 355 older community dwelling adults (mean age = 77). RESULTS: After multivariate analysis, 2 questions were excluded. All the questions from the original CFAI were implemented in a principal component analysis together with the 4 cognitive questions, showing that the 4 cognitive questions all load on 1 factor, representing the cognitive domain of frailty. By adding the cognitive domain to the CFAI, the reliability of the adapted CFAI (CFAI-Plus), remains good (Cronbach's alpha: .767). CONCLUSIONS: This study showed that cognitive frailty can be added to the CFAI without affecting its good psychometric properties. In the future, the CFAI-Plus needs to be validated in an independent cohort, and the interaction with the other frailty domains needs to be studied.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".