Amyloid β-related Angiitis of the Central Nervous System: Report of 3 Cases
Bibliographic record
Abstract
OBJECTIVE: Amyloid-β (Aβ) related angiitis (ABRA) is a recently described clinicopathological entity characterized by cerebrovascular Aβ deposition and arteritis. Cerebral Aβ deposition is commonly present in cerebal amyloid angiopathy (CAA) and Alzheimer's disease (AD) but is rarely associated with inflammatory infiltration of vessel walls. Our objective is to help clarify the clinical spectrum, radiographic findings, response to treatment, and outcomes of ABRA. The neuropathological relationship between ABRA, cerebral amyloid angiopathy, and Alzheimer's disease is discussed. METHODS: We present three cases of ABRA managed at a tertiary care centre. RESULTS: All three patients presented with seizures and cognitive dysfunction; one had multifocal neurologic findings. Brain biopsies revealed inflammatory arteritis with Aβ deposits in the vessel walls. All were treated with steroids and cyclophosphamide. Two had favorable outcomes and one stabilized but with severe residual neurologic disability. CONCLUSIONS: ABRA is an unusual but likely under-recognized and potentially treatable disorder. As in other reported cases, our findings suggest that many patients respond favorably to immunosuppressive therapy. We believe that all biopsy specimens consistent with primary angiitis of the central nervous system (CNS) should be further examined for vascular Aβ deposition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".