Towards a histological diagnosis of childhood small vessel CNS vasculitis
Bibliographic record
Abstract
BACKGROUND: Primary small vessel CNS vasculitis (sv-cPACNS) is a challenging inflammatory brain disease in children. Brain biopsy is mandatory to confirm the diagnosis. This study aims to develop and validate a histological scoring tool for diagnosing small vessel CNS vasculitis. METHODS: A standardized brain biopsy scoring instrument was developed and applied to consecutive full-thickness brain biopsies of pediatric cases and controls at a single center. Stains included immunohistochemistry and Hematoxylin & Eosin. Nine North American neuropathologists, blinded to patients' presentation, diagnosis, and therapy, scored de-identified biopsies independently. RESULTS: A total of 31 brain biopsy specimens from children with sv-cPACNS, 11 with epilepsy, and 11 with non-vasculitic inflammatory brain disease controls were included. Angiocentric inflammation in the cortex or white matter increases the likelihood of sv-cPACNS, with odds ratios (ORs) of 3.231 (95CI: 0.914-11.420, p = 0.067) and 3.923 (95CI: 1.13-13.6, p = 0.031). Moderate to severe inflammation in these regions is associated with a higher probability of sv-cPACNS, with ORs of 5.56 (95CI: 1.02-29.47, p = 0.046) in the cortex and 6.76 (95CI: 1.26-36.11, p = 0.025) in white matter. CD3, CD4, CD8, and CD20 cells predominated the inflammatory infiltrate. Reactive endothelium was strongly associated with sv-cPACNS, with an OR of 8.93 (p = 0.001). Features reported in adult sv-PACNS, including granulomas, necrosis, or fibrin deposits, were absent in all biopsies. The presence of leptomeningeal inflammation in isolation was non-diagnostic. CONCLUSION: Distinct histological features were identified in sv-cPACNS biopsies, including moderate to severe angiocentric inflammatory infiltrates in the cortex or white matter, consisting of CD3, CD4, CD8, and CD20 cells, alongside reactive endothelium with specificity of 95%. In the first study of its kind proposing histological criteria for evaluating brain biopsies, we aim to precisely characterize the type and severity of the inflammatory response in patients with sv-cPACNS; this can enable consolidation of this population to assess outcomes and treatment methodologies comprehensively.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".