Primary central nervous system vasculitis in children
Bibliographic record
Abstract
OBJECTIVE: Primary angiitis of the central nervous system (PACNS) is a severe and ill-defined neurologic disease. The goal of this study was to characterize the presenting features, treatment, and neurologic outcome of PACNS in children (cPACNS) and to define the predictors of disease progression in order to identify high-risk patients with cPACNS. METHODS: The cohort comprised consecutive patients diagnosed as having cPACNS based on clinical and vascular imaging findings, including identification of arterial stenosis on conventional angiography or magnetic resonance (MR) angiography. Disease progression was defined angiographically at >3 months after initial angiography. Clinical data obtained in prospectively collected standardized assessments and results of laboratory tests, including detection of cerebrospinal fluid abnormalities, were noted, and neuroimaging studies were reanalyzed. Predictors of progression were identified and tested in multivariate regression models. RESULTS: Sixty-two consecutive patients with cPACNS (38 male, 24 female, median age 7.2 years) were included. Two distinct subgroups were identified, those with progressive disease and those with nonprogressive disease. Progressive cPACNS was found in 20 of 62 children and was predicted by a clinical presentation of neurocognitive dysfunction, multifocal parenchymal lesions on MR imaging, and evidence of distal stenoses on angiography. CONCLUSION: The spectrum of PACNS in children includes both progressive and nonprogressive forms. Characteristic features at diagnosis can be used to predict later progression, to identify a distinct high-risk cPACNS cohort, and to help guide selection of patients for immunosuppressive therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".