Arterial Wall Imaging in Pediatric Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Arteriopathy is common in childhood arterial ischemic stroke (AIS) and predicts stroke recurrence. Currently available vascular imaging techniques mainly image the arterial lumen rather than the vessel wall and have a limited ability to differentiate among common arteriopathies. We aimed to investigate the value of a magnetic resonance imaging-based technique, namely noninvasive arterial wall imaging (AWI), for distinguishing among arteriopathy subtypes in a consecutive cohort of children presenting with AIS. METHODS: Children with confirmed AIS and magnetic resonance angiography underwent 3-Tesla AWI including T1-weighted 2-dimensional fluid-attenuated inversion recovery fast spin echo sequences pre- and post-gadolinium contrast. AWI characteristics, including wall enhancement, wall thickening, and luminal stenosis, were documented for all. RESULTS: Twenty-six children with AIS had AWI. Of these, 9 (35%) had AWI enhancement. AWI enhancement was associated with anterior circulation magnetic resonance angiography abnormality and cortical infarction in 8 of 9 (89%) children and normal magnetic resonance angiography with posterior circulation subcortical infarction in 1 (1 of 9; 11%) child. AWI enhancement was not seen in 17 (65%), 10 (59%) of whom had an abnormal magnetic resonance angiography. Distinct patterns of pre- and postcontrast signal abnormality were demonstrated in the vessel wall in the region of interest in children with transient cerebral arteriopathy, arterial dissection, primary central nervous system angiitis, dissecting aneurysm, and cardioembolic stroke. CONCLUSIONS: AWI is a noninvasive, high-resolution magnetic resonance AWI technique, which can be successfully used in children presenting with AIS. Patterns of AWI enhancement are recognizable and associated with specific AIS pathogeneses. Further studies are required to assess the additional diagnostic utility of AWI over routine vascular imaging techniques, in childhood AIS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".