Moyamoya Across the Lifespan
Bibliographic record
Abstract
gene, most common in Asian populations, is associated with severe, early onset, multisystem vasculopathy. Neuroimaging is critical for moyamoya diagnosis and treatment planning, with conventional imaging, catheter angiography, perfusion imaging, and cerebrovascular reactivity assessment all having a place within moyamoya care. Medical management of moyamoya entails reducing the competing risks of ischemic and hemorrhagic stroke as well as managing coexisting conditions, such as headache, epilepsy, and neuropsychological sequelae. Antiplatelet therapy is commonly prescribed to prevent thromboembolic stroke, although data supporting this practice are limited and practice patterns vary globally. Promoting cerebral oxygen and nutrient delivery with sufficient fluid intake, maintaining adequate blood pressure, and avoiding anemia and hypoglycemia aid in stroke prevention in moyamoya. Definitive treatment of moyamoya is predicated on surgical revascularization, which aims to augment perfusion to at-risk brain tissue and decrease the risk of hemorrhage from fragile moyamoya collaterals. Although surgery is highly effective in appropriately selected patients, perioperative ischemic events occur following 4%-18% of cases. Perioperative medical management aims to mitigate this risk by optimizing brain oxygen delivery through adequate cerebral perfusion and blood oxygenation, pain/nausea control, minimizing metabolic demand, and preventing thrombosis. Long-term neuroimaging surveillance, evaluation of the neuropsychological effect of moyamoya, and screening for and management of headache and epilepsy resulting from moyamoya are important aspects of the chronic care of all patients with moyamoya. In this review, we summarize key aspects of neurologic evaluation and management for moyamoya across the lifespan, highlight key differences between adult and pediatric moyamoya, and discuss ongoing research efforts that aim to improve care of children and adults 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".