Gait in Cerebral Amyloid Angiopathy
Bibliographic record
Abstract
Background Gait is a complex task requiring coordinated efforts of multiple brain networks. To date, there is little evidence on whether gait is altered in cerebral amyloid angiopathy (CAA). We aimed to identify impairments in gait performance and associations between gait impairment and neuroimaging markers of CAA, cognition, and falls. Methods and Results Gait was assessed using the Zeno Walkway during preferred pace and dual task walks, and grouped into gait domains (Rhythm, Pace, Postural Control, and Variability). Participants underwent neuropsychological testing and neuroimaging. Falls and fear of falling were assessed through self-report questionnaires. Gait domain scores were standardized and analyzed using linear regression adjusting for age, sex, height, and other covariates. Participants were patients with CAA (n=29), Alzheimer disease with mild dementia (n=16), mild cognitive impairment (n=24), and normal elderly controls (n=47). CAA and Alzheimer disease had similarly impaired Rhythm, Pace, and Variability, and higher dual task cost than normal controls or mild cognitive impairment. Higher Pace score was associated with better global cognition, processing speed, and memory. Gait measures were not correlated with microbleed count or white matter hyperintensity volume. Number of falls was not associated with gait domain scores, but participants with low fear of falling had higher Pace (odds ratio [OR], 2.61 [95% CI, 1.59-4.29]) and lower Variability (OR, 1.64 [95% CI, 1.10-2.44]). Conclusions CAA is associated with slower walking, abnormal rhythm, and greater gait variability than in healthy controls. Future research is needed to identify the mechanisms underlying gait impairments in CAA, and whether they predict future falls.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".