Diagnosing central nervous system vasculitis in children
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the current literature of childhood central nervous system vasculitis, and to discuss a tailored approach to diagnosis and treatment based on recent evidence. RECENT FINDINGS: Primary angiitis of the central nervous system in children (cPACNS) is an increasingly recognized inflammatory brain disease with potentially devastating neurological consequences. The diagnostic approach should be tailored to the clinical presentation of the child with suspected cPACNS and should address the expanding spectrum of inflammatory and noninflammatory brain diseases with overlapping clinical features. New evidence has confirmed that elective brain biopsies in children have a higher diagnostic yield than in adults and improve our ability to diagnose angiography-negative cPACNS. Finally, observational studies have shown that early diagnosis and aggressive treatment lead to improved neurological outcomes and lower mortality rates in patients with cPACNS. SUMMARY: This review summarizes the recent data on diagnosis, classification, treatment, and outcomes in cPACNS. Our improved understanding of cPACNS facilitates a tailored diagnostic approach that results in earlier diagnosis and initiation of therapy for this potentially reversible condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".