Frailty Phenotype and Neuropsychological Test Performance: A Preliminary Analysis
Bibliographic record
Abstract
CONTEXT: Frailty is a common problem that affects adults older than 65 years. Correlations between the frailty phenotype and neuropsychological impairment have not been thoroughly researched. OBJECTIVE: To examine the association between frailty phenotype, neuropsychological screening test results, and neuropsychological domains known to characterize patients with mild cognitive impairment and dementia. METHODS: This retrospective medical record analysis consisted of ambulatory patients aged 65 years or older seen in an outpatient geriatric practice. All patients were assessed with the Montreal Cognitive Assessment (MoCA). A portion of those patients also underwent a comprehensive neuropsychological evaluation that assessed executive control, naming/lexical access, and declarative memory expressed as 3 neuropsychological index scores. Frailty phenotype was determined using criteria by Fried et al. RESULTS: Simple correlation found that lower MoCA test scores were associated with a higher level of frailty (r=-0.34, P<.032). Regression analyses found that greater frailty was associated with worse performance on tests that assessed executive control and working memory (backward digit span; r2=0.267; β=-0.517; P<.011) and delayed recognition memory (r2=0.207; β=-0.455; P<.025). CONCLUSION: A correlation was found between frailty and neuropsychological impairment, which suggests that frailty may be a potential indicator for the emergence of mild cognitive impairment and dementia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".