P1‐407: Cognitive Frailty: A New Domain Added to The Comprehensive Frailty Assessment
Bibliographic record
Abstract
Frailty is a complex and multidimensional syndrome. In order to detect frailty, different instruments are developed. The Comprehensive Frailty Assessment (CFAI), a promising and already widely used instrument to detect frailty, measures four domains of frailty, namely physical, psychological, social, and environmental frailty but does not include cognitive frailty. Cognitive frailty is associated with negative outcomes such as a higher chance to develop dementia and a higher rate of institutionalization. The absence of a measure of cognitive frailty can be seen as an important limitation for this instrument. The goal of this study is to add cognitive frailty as a domain to the CFAI. In this study, six questions about older person’s cognition, the Montreal Cognitive Assessment (MoCA), and the CFAI were administered to 100 community dwelling older persons. These six questions are based on the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), which is a short questionnaire designed to assess cognitive decline and diagnose dementia in older adults. The MoCA is an objective measure for cognitive functioning. Data collection will be finished February 12016. The correlation between the objective measure for cognitive decline (MoCA) and the interpretation of the participants (six questions) will be studied. Based on this association we will select, through factor analysis, the most useful questions and add them to the CFAI. The addition of a cognitive frailty domain to the CFAI will make the instrument sensitive to cognitive decline. This can help to make preventive actions more attuned to the needs of people at risk for cognitive impairment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".