Impact of Brain MRI Markers on Major and Mild Vascular Cognitive Impairment in CADASIL
Bibliographic record
Abstract
Background: Cognitive impairment is the second most common clinical manifestation in cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). However, understanding of cognitive impairment in CADASIL has been hampered by lack of consensus on diagnosis of vascular cognitive impairment (VCI). We used vascular impairment of cognition classification consensus study principles (VICCCS-1) and protocols (VICCCS-2) to assess the cognitive impairment in CADASIL. We also evaluated the impact of MRI markers on major and mild VCI in CADASIL.Methods: We prospectively recruited 64 patients who underwent standardized brain MRI and detailed neuropsychological test. MRI analysis included number of lacunes, number of cerebral microbleeds (CMB), normalized volume of white-matter hyperintensities (nWMH), and brain parenchymal fraction (BPF). BPF has been used to measure brain atrophy. The patients were divided into three groups: those with normal cognition (CADASIL-NC, n=14), those with mild VCI (CADASIL-mild VCI, n=38), and those with major VCI (CADASIL-major VCI, n=11).Results: The three groups differed according to age, with the major VCI group being older. The major VCI group had more lacunes, more CMB, more extensive white matter lesions and lower BPF than NC group. There were no significant differences between NC and mild VCI groups in BPF. BPF and age were the independent predictors of major VCI. There was a tendency that women were at higher risk for mild VCI, though it did not reach statistical significance. Women were older than men, but had lower number of lacunes in mild VCI.Conclusions: These findings suggest that brain atrophy and age are the main predictors of major VCI in CADASIL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".