Predicting Ischemic Risk Using Blood Oxygen Level–Dependent MRI in Children with Moyamoya
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Moyamoya is a progressive steno-occlusive arteriopathy. MR imaging assessment of cerebrovascular reactivity can be performed by measuring the blood oxygen level–dependent cerebrovascular reactivity response to vasoactive stimuli. Our objective was to determine whether negative blood oxygen level–dependent cerebrovascular reactivity status is predictive of ischemic events in childhood moyamoya. MATERIALS AND METHODS: We conducted a retrospective study of a consecutive cohort of children with moyamoya who underwent assessment of blood oxygen level–dependent cerebrovascular reactivity. The charts of patients with written informed consent were reviewed for the occurrence of arterial ischemic stroke, transient ischemic attack, or silent infarcts. We used logistic regression to calculate the OR and 95% CI for ischemic events based on steal status. Hazard ratios for ischemic events based on age at blood oxygen level–dependent cerebrovascular reactivity imaging, sex, and moyamoya etiology were calculated using Cox hazards models. RESULTS: Thirty-seven children (21 female; median age, 10.7 years; interquartile range, 7.5–14.7 years) were followed for a median of 28.8 months (interquartile range, 13.7–84.1 months). Eleven (30%) had ischemic events, 82% of which were TIA without infarcts. Steal was present in 15 of 16 (93.8%) hemispheres in which ischemic events occurred versus 25 of 58 (43.1%) ischemic-free hemispheres (OR = 19.8; 95% CI, 2.5–160; P = .005). Children with idiopathic moyamoya were at significantly greater risk of ischemic events (hazard ratio, 3.71; 95% CI, 1.1–12.8; P = .037). CONCLUSIONS: Our study demonstrates that idiopathic moyamoya and the presence of steal are independently associated with ischemic events. The use of blood oxygen level–dependent cerebrovascular reactivity could potentially assist in the selection of patients for revascularization surgery and the direction of therapy in children with moyamoya.
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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 | 0.003 |
| 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 | 0.001 |
| 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".