Predictors of Cerebral Arteriopathy in Children With Arterial Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Cerebral arteriopathies, including an idiopathic focal cerebral arteriopathy of childhood (FCA), are common in children with arterial ischemic stroke and strongly predictive of recurrence. To better understand these lesions, we measured predictors of arteriopathy within a large international series of children with arterial ischemic stroke. METHODS AND RESULTS: Between January 2003 and July 2007, 30 centers within the International Pediatric Stroke Study enrolled 667 children (age, 29 days to 19 years) with arterial ischemic stroke and abstracted clinical and radiographic data. Cerebral arteriopathy and its subtypes were defined using published definitions; FCA was defined as cerebral arterial stenosis not attributed to specific diagnoses such as moyamoya, arterial dissection, vasculitis, or postvaricella angiopathy. We used multivariate logistic regression techniques to determine predictors of arteriopathy and FCA among those subjects who received vascular imaging. Of 667 subjects, 525 had known vascular imaging results, and 53% of those (n=277) had an arteriopathy. The most common arteriopathies were FCA (n=69, 25%), moyamoya (n=61, 22%), and arterial dissection (n=56, 20%). Predictors of arteriopathy include early school age (5 to 9 years), recent upper respiratory infections, and sickle cell disease, whereas prior cardiac disease and sepsis reduced the risk of arteriopathy. The only predictor of FCA was recent upper respiratory infection. CONCLUSIONS: Arteriopathy is prevalent among children with arterial ischemic stroke, particularly those presenting in early school age, and those with a history of sickle cell disease. Recent upper respiratory infection predicted cerebral arteriopathy and FCA in particular, suggesting a possible role for infection in the pathogenesis of these lesions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".