Abstract WMP107: Arterial Tortuosity: an Imaging Biomarker of Perinatal Stroke Pathogenesis?
Bibliographic record
Abstract
Background: Perinatal stroke causes cerebral palsy and lifelong disability. Specific diseases are definable but pathophysiological mechanisms are unknown. Arterial tortuosity may reflect inherent vascular biology. We recently demonstrated an association between abnormal tortuosity and specific arteriopathies in childhood stroke. Hypothesis: As fetal periventricular infarction (PVI) has been associated with genetic connective tissue disease, we hypothesized that tortuosity would be increased in PVI compared to other perinatal stroke diseases and controls. Methods: Subjects were recruited from the Alberta Perinatal Stroke Project (APSP), a population-based research cohort. Inclusion criteria were MRI-classified perinatal stroke syndrome and 3D time-of-flight MR angiography (MRA). A validated imaging software method calculated cerebral arterial tortuosity using source images. Mean tortuosity scores (ANOVA) and variances (Levene’s test) were compared across arterial ischemic, hemorrhagic, and PVI strokes and controls. Effects of age and gender were examined. Results: A total of 130 children were studied (61 arterial, 30 PVI, 15 hemorrhagic, 24 controls). Tortuosity scores and variances were consistent with validation studies. Tortuosity was not associated with age at imaging (including infants versus older children) or gender. Variance in tortuosity was much greater in all perinatal stroke groups compared to controls (p<0.0008) with no differences between specific stroke types found. There was no difference in mean tortuosity scores across groups. Conclusion: Children with perinatal stroke have an abnormally wide range of arterial tortuosity, suggesting inherent differences in vascular biology. This is consistent with genetic connective tissue disorders associated with PVI while suggesting novel mechanisms for other perinatal stroke diseases.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".