Abstract 169: Clinical and Imaging Biomarkers of Childhood Arteriopathy: Results of the Vascular effects of Infection in Pediatric Stroke (VIPS) Study
Bibliographic record
Abstract
Introduction: Childhood cerebral arteriopathies are heterogeneous and difficult to classify. Our goal was to identify clinical and imaging biomarkers associated with subtypes of childhood arteriopathy in order to facilitate their diagnosis and classification in both research and clinical settings. Methods: From 2009-2014, the VIPS study enrolled 355 children (age 29d-18y) with arterial ischemic stroke. A neuroradiologist and pediatric vascular neurologist independently reviewed vascular imaging and clinical data to diagnose arteriopathy (none, possible, definite), and then classify arteriopathy subtype: arterial dissection, transient cerebral arteriopathy (TCA), moyamoya, and secondary vasculitis. Disagreements were resolved through discussion by a panel of two neuroradiologists and two pediatric vascular neurologists. We constructed multivariable logistic regression models to identify characteristics independently associated with each subtype. Results: Of 127 cases with definite arteriopathy, 109 received a single arteriopathy subtype diagnosis (26 dissection, 25 TCA, 34 moyamoya, 15 secondary vasculitis, and 9 “other”), while 18 were of indeterminate subtype. There were no cases of primary vasculitis. Independent predictors of arteriopathy subtype are shown (Table). The association between black race and moyamoya is explained by sickle cell anemia. A banding pattern on the vascular imaging was pathognomonic of TCA. Moyamoya and vasculitis secondary to meningitis had similar distal internal carotid artery abnormalities, but lenticulostriate collaterals suggested moyamoya, and a decreased level of consciousness predicted secondary vasculitis. Conclusions: The different subtypes of childhood arteriopathies are associated with typical clinical, parenchymal and vascular imaging features that can help narrow the differential diagnosis in pediatric stroke patients with vascular abnormalities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".