EFFECTS OF AMYLOID ON CHANGES IN COGNITIVE AND PHYSICAL FUNCTION IN VASCULAR COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Alzheimer’s disease (AD) and subcortical ischemic vascular cognitive impairment (SIVCI) are the two most common causes of cognitive impairment and often patients present with mixed AD-SIVCI pathology. Currently, much of our knowledge on the effects of co-existing amyloid pathology in SIVCI are based on cross-sectional studies and little is known about their effects on changes in cognitive and physical function over time. Thus, the objective of this study was to assess the impact of amyloid pathology on global cognitive, executive functions, and physical function over a 12-month period in people with SIVCI. This was a planned secondary analysis of data acquired from a proof-of-concept randomized controlled trial of aerobic activity in people with SIVCI. A hierarchical multiple linear regression analysis was conducted to determine the unique contribution of amyloid pathology on cognitive/physical function after controlling for age, experimental group assignment, and baseline cognitive/physical performance. We found that amyloid pathology significantly predicted decreased performance in: 1) Attention (Stroop Test – adjusted R-square change of 32.0%, p < 0.05); 2) Set shifting (Trail Making Test – adjusted R-square change of 34.7%, p < 0.05); 3) Processing speed (Digit Symbol Substitution Test – adjusted R-square change of 35.0%, p < 0.05) and; 4) Falls risk (Physiological Profile Assessment – adjusted R-square change of 9.0%, p < 0.05). However, amyloid did not predict changes in global cognition or working memory (p > 0.05). Our study suggests that amyloid plaques might be a marker for future decline in cognitive and physical performance among older adults with a primary diagnosis of SIVCI.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".