Relationship between frailty and Alzheimer's disease biomarkers: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Frailty and dementia appear to be closely linked, although mechanisms remain unclear. The objective was to conduct a scoping review of the association between frailty and Alzheimer's disease (AD) biomarkers in humans. METHODS: Three databases, PubMed, PsycINFO, and Embase, were searched for articles using the following search terms: "frail elderly", "Alzheimer's disease", "dementia biomarkers" and their synonyms. Inclusion was limited to original research in humans published before 2017, which included a frailty measure and AD biomarker (fluid markers, neuroimaging, and neuropathology). RESULTS: Five hundred twenty-two articles were identified and screened; 10 were included. Most were cross-sectional (n = 6), measured the frailty phenotype (n = 6), and included people with dementia (n = 7). Biomarkers examined were postmortem AD pathology (n = 3), brain atrophy (n = 5), and in vivo fluid markers (n = 2). Eight studies reported that increased frailty was associated with at least one biomarker abnormality. DISCUSSION: Evidence is limited and suffers from design limitations but suggests that frailty and AD biomarkers are closely linked. Longitudinal research examining multiple biomarkers and frailty is warranted.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".