Arterial tortuosity: an imaging biomarker of childhood stroke pathogenesis?
Bibliographic record
Abstract
Background: Arteriopathy causes most childhood arterial ischemic stroke (AIS). Mechanisms are poorly understood but may include abnormalities of arterial structure. Extracranial dissection is common while intracranial dissection may explain idiopathic focal cerebral arteriopathy (FCA). We aimed to quantify cerebral arterial tortuosity and hypothesized increased tortuosity in extracranial dissection. Methods: Children with AIS were recruited within the Vascular-Effects-of-Infection-in-Pediatric-Stroke (VIPS) study (controls from the Calgary Pediatric Stroke Program). A validated software method calculated mean tortuosity of major cerebral arteries using 3D time-of-flight MR angiography (MRA). Blinded, multi-investigator reviews defined diagnostic categories. Tortuosity was compared between dissection (spontaneous and traumatic), FCA, moyamoya, meningitis, and cardioembolic, and controls (ANOVA, post-hoc Tukey). Results: A total of 116 children were studied. Age and gender were comparable across groups. Tortuosity scores and variances were consistent with validation studies. Tortuosity in controls (1.333±0.039, n=15) was comparable to moyamoya (1.324±0.038, p=0.99, n=15), meningitis (1.348±0.052, p=0.98, n=12) and cardioembolic (1.379±0.056, p=0.19, n=27) cases. Tortuosity was higher in dissection (1.398±0.072, p=0.02, n=22) and FCA (1.421±0.076, p=0.001, n=25). Traumatic (1.391±0.036, n=9) and non-traumatic (1.403±0.090, p=0.671, n=13) scores were not different. Conclusion: Children with dissection have more tortuous arteries. Quantified tortuosity may represent a clinically relevant biomarker of vascular biology in pediatric stroke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".