Alzheimer's disease with frailty: Prevalence, screening, assessment, intervention strategies and challenges
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide and is expected to surge in prevalence due to aging populations. Frailty, characterized by muscle function decline, becomes more prevalent with age, imposing substantial burdens on patients and caregivers. This paper aimed to comprehensively review the current literature on AD coupled with frailty, encompassing prevalence, screening, assessment, and treatment while delving into the field's challenges and future trajectories. Frailty and AD coexist in more than 30% of cases, with hazard ratios above 120% indicating a mutually detrimental association.Various screening tools have emerged for both frailty and AD, including the Fried Frailty Phenotype (FP), FRAIL scale, Edmonton Frailty Scale (EFS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clock Drawing Test (CDT), and General Practitioner Assessment of Cognition (GPCOG). However, none has solidified its role as the definitive gold standard. The convergence of electronic health records and brain aging biomarkers heralds a new era in AD with frailty screening and assessment. In terms of intervention, non-pharmacological strategies spanning nutrition, horticulture, exercise, and social interaction, along with pharmacological approaches involving acetylcholinesterase inhibitors (AChEIs), N-methyl-D-aspartate (NMDA) receptor antagonists, and anti-amyloid beta-protein medications, constituted cornerstones for treating AD coupled with frailty. Technological interventions like repetitive transcranial magnetic stimulation (rTMS) also entered the fold. Notably, multi-domain non-pharmacological interventions wield considerable potential in enhancing cognition and mitigating disability. However, the long-term efficacy and safety of pharmacological interventions necessitate further validation. Diagnosing and managing AD with frailty present several daunting challenges, encompassing low rates of early co-diagnosis, limited clinical trial evidence, and scarce integrated, pioneering service delivery models. These challenges demand heightened attention through robust research and pragmatic implementation.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".