Differences in rate of functional decline across three dementia types
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to estimate differences in rates of functional decline in Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and vascular dementia (VaD) and whether differences vary by age or sex. METHODS: Data came from 32 U.S. Alzheimer's Disease Centers. The cohort of participants (n = 5848) were ≥60 years of age and had clinical dementia with a primary etiologic diagnosis of probable AD, DLB, or probable VaD; a Clinical Dementia Rating-Sum of Boxes score <16; and a duration of symptoms ≤10 years. Dementia diagnoses were assigned using standard criteria. Annual mean rate of change of the Functional Activities Questionnaire (FAQ) score was modeled using multiple linear regression with generalized estimating equations adjusted for demographics, comorbidities, years since onset, and cognitive status (mean follow-up = 2.0 years). RESULTS: FAQ declined more slowly over time in those with VaD compared with AD (difference in mean annual rate of change: -0.91; 95% confidence interval [CI]: -1.68, -0.14). VaD participants also declined at a slower rate than DLB participants, but this difference was not statistically significant (-0.61; 95% CI: -1.45, 0.24). There was no significant difference between DLB and AD. Within each group, rate of decline was more rapid for the youngest participants. CONCLUSIONS: In this sample, findings suggested that VaD patients declined in their functional abilities at a slower rate compared with AD patients and that there were no significant differences in rate of functional decline between patients with DLB compared with those with either AD or VaD. These results may provide guidance to clinicians about average expected rates of functional decline in three common dementia types.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".