A systematic review of assessment tools for cognitive frailty: Use, psychometric properties, and clinical utility
Bibliographic record
Abstract
BACKGROUND: The concept of 'cognitive frailty' (CF) was first developed by an international consensus group in 2013 and defined as evidence of both physical frailty and cognitive impairment without a clinical diagnosis of AD or another dementia. CF has been associated with adverse health outcomes and early identification is vital. Difficulty in the assessment of CF however is the lack of a diagnostic gold standard. OBJECTIVES: This review aimed to identify assessment tools used to diagnose cognitive impairment in the diagnosis of cognitive frailty, their psychometric qualities and clinical utility. RESEARCH DESIGN AND METHODS: Six databases were searched between 2013-2024. Studies were eligible if they reported a method of defining cognitive frailty, named the assessment tools, and stated cutoff values used to define cognitive impairment. RESULTS: In the 116 included studies, large heterogeneity was found in the tools utilised, and cutoff scores applied, to diagnose cognitive impairment in the diagnosis of cognitive frailty. This review has demonstrated that diagnosis of CF relies predominantly on the use of three cognitive assessment tools (Mini Mental State Examination, Montreal Cognitive Assessment, Clinical Dementia Rating) from a total of 22 different tools identified in the literature. For assessment of physical frailty, 11 different tools were identified, with the Fried Frailty Index and FRAIL Scale predominantly utilised. DISCUSSION AND IMPLICATIONS: The variation in the tools used to identify the diagnosis of CF means there is inconsistency in reporting, potentially impacting both the understanding of the prevalence, and the appropriate direction of intervention strategies.
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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 | 0.019 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".