Prevalence, and Predictors, of Vascular Cognitive Impairment in CADASIL
Bibliographic record
Abstract
Abstract Background and Objective CADASIL is the most common monogenic form of stroke and early onset dementia. We determined the prevalence of vascular cognitive impairment (VCI) in a cohort of CADASIL patients, and investigated which factors were associated with VCI risk, including clinical, genetic and MRI parameters. Methods Cognition was assessed in genetically confirmed CADASIL patients (n = 176) and healthy controls (n= 265) (mean(SD) age 50.95(11.35) v 52.37(7.93) years), using the Brief Memory and Executive Test (BMET) and the Montreal Cognitive Assessment (MoCA). VCI was defined according to previously validated cut-offs. We determined the prevalence of VCI and its associations with clinical risk factors, mutation location (EGFr 1-6 versus EGFr 7-34), and MRI markers of small vessel disease. Results VCI was more common in CADASIL than controls; 39.8 v 10.2% on BMET 47.7% v 19.6% of MOCA. CADASIL patients had worse performance across all cognitive domains. History of stroke was associated with VCI on the BMET (OR 2.12, 95% CI [1.05, 4.27] p = 0.04) and on the MoCA (OR 2.55 [1.21, 5.41] p = 0.01), after controlling for age and sex. There was no association of VCI with mutation site. Lacune count was the only MRI parameter independently associated with VCI on the BMET (OR: 1.63, 95% CI [1.10, 2.41], p = 0.014), after controlling for other MRI parameters. These associations persisted after controlling for education in the sensitivity analyses. Conclusions VCI is present in almost half of CADASIL patients with a mean age of 50. Stroke and lacune count on MRI were both independent predictors of VCI on the BMET.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".