Towards a histological diagnosis of childhood small vessel CNS vasculitis. (S35.003)
Bibliographic record
Abstract
OBJECTIVE: To systematically review biopsies of Childhood primary small vessel CNS vasculitis (SVcPACNS) patients and inflammatory and epilepsy controls and to determine characteristic features defining the diagnosis of SVcPACNS. BACKGROUND: SVcPACNS is an increasingly recognized inflammatory brain disease with high morbidity and mortality mandating an elective brain biopsy to confirm the diagnosis. DESIGN/METHODS: A previously developed, standardized brain biopsy review instrument was applied to consecutive full thickness brain biopsies of pediatric cases and controls collected at a single center. Standardized stains including Hematoxyllin & Eosin, histochemistry of immune cell subsets plus electron microscopy. Nine North American expert neuropathologists were blinded reviewed to the patient’s presentation, diagnosis and therapy. All biopsies were de-identified and scored independently by two reviewers. Univariate analyses compared variable between groups; correspondence analysis determined the multi-dimensional relationship of histological variables and patient diagnoses. RESULTS: A total of 31 brain biopsy specimens of children with SVcPACNS, 12 with epilepsy and 11 with non-vasculitic inflammatory brain disease controls were included. Correspondence analyses revealed distinct clusters of the three diagnoses based on dimensions of location of infiltrate and subtype/ severity of inflammation. Significant histological characteristics found to set apart SVcPACNS from controls included angiocentric (p<0.01) and/or perivascular infiltrates (p=0.04), evidence of endothelial cell activation (p<0.01) and inflammation in both grey and white matter (p<0.01). The infiltrate was found to be primarily T-cell mediated (CD3+ 86[percnt], CD8+ 90[percnt]) only 27[percnt] of SVcPACNS biopsies had evidence of B cells. Features reported in adult PACNS including granulomas, necrosis or fibrin deposits were absent in all biopsies. Leptomeningeal inflammation was non-diagnostic. CONCLUSIONS: Distinct histological features were identified on brain biopsies of SVcPACNS and may help define the disease. These were absent in biopsies of children with epilepsy and non-vasculitic inflammatory brain diseases and allow for the development of diagnostic criteria.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".