P.080 Cognitive Profile, Disease Characteristics, and Neuroimaging Findings in Susac Syndrome: A Case Series of Seven Participants from British Columbia
Bibliographic record
Abstract
Background: Susac Syndrome (SuS) is a rare autoimmune disorder of the cerebral, retinal, and inner ear microvasculature. One of the cardinal manifestations of central nervous system (CNS) involvement is encephalopathy, however the cognitive profile in SuS is poorly characterized in the literature. Methods: In this cross-sectional case series of seven participants diagnosed with Susac Syndrome in remission in British Columbia, we use a battery of neuropsychological testing, subjective disease scores, and objective markers of disease severity to characterize the affected cognitive domains and determine if any disease characteristics predict neuropsychological performance. We also compare this battery of tests to neuroimaging markers to determine if correlation exists between radiographic markers of CNS disease and clinical evaluation of disease severity. Results: There were a variety of cognitive deficits, with memory and language dysfunction being the most common. Despite the variability, performance on some neuropsychological tests (MoCA) correlated to markers of functional disability (EDSS). Additionally, MoCA and EDSS scores correlated with neuroimaging findings of both corpus callosum and white matter changes. Finally, psychiatric scores correlated with participant reported scores of disease severity. Conclusions: There is a relationship between cognitive deficits, subjective and objective disease disability, and neuroimaging findings in Susac Syndrome.
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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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".