Spectrum of cerebral arteriopathies in children with arterial ischemic stroke
Bibliographic record
Abstract
Objective To determine that children with arterial ischemic stroke (AIS) due to an identifiable arteriopathy are distinct from those without arteriopathy and that each arteriopathy subtype has unique and recognizable clinical features. Methods We report a large, observational, multicenter cohort of children with AIS, age 1 month to 18 years, enrolled in the International Pediatric Stroke Study from 2003 to 2014. Clinical and demographic differences were compared by use of the Fisher exact test, with linear step-up permutation min-p adjustment for multiple comparisons. Exploratory analyses were conducted to evaluate differences between cases of AIS with and without arteriopathy and between arteriopathy subtypes. Results Of 2,127 children with AIS, 725 (34%) had arteriopathy (median age 7.45 years). Arteriopathy subtypes included dissection (27%), moyamoya (24.5%), focal cerebral arteriopathy–inflammatory subtype (FCA-i; 15%), diffuse cerebral vasculitis (15%), and nonspecific arteriopathy (18.5%). Children with arteriopathic AIS were more likely to present between 6 and 9 years of age (odds ratio [OR] 1.93, p = 0.029) with headache (OR 1.55, p = 0.023), multiple infarctions (OR 2.05, p < 0.001), sickle cell anemia (OR 2.9, p = 0.007), and head/neck trauma (OR 1.93, p = 0.018). Antithrombotic use and stroke recurrence were higher in children with arteriopathy. Among arteriopathy subtypes, dissection was associated with male sex, older age, headache, and anticoagulant use; FCA-i was associated with hemiparesis and single infarcts; moyamoya was associated with seizures and recurrent strokes; and vasculitis was associated with bilateral infarctions. Conclusion Specific clinical profiles are associated with cerebral arteriopathies in children with AIS. These observations may be helpful indicators in guiding early diagnosis and defining subgroups who may benefit most from future therapeutic trials.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".