Investigating Neuropsychological and Mental Health Outcomes After Surgical Intervention in Pediatric Moyamoya Vasculopathy
Bibliographic record
Abstract
Moyamoya vasculopathy (MMV), a rare and progressive cerebrovascular disorder, is an important cause of pediatric stroke. This retrospective study explored post-revascularization neuropsychological and mental health outcomes. Eighteen patients from the Canadian Pediatric Stroke Registry at the Hospital for Sick Children (MAge=10, SD=4.073) who had revascularization surgery and completed neuropsychological assessments between 2012 and 2024 were selected. Outcome measures included academics, executive functioning, and mental health. Predictor variables were age at revascularization surgery, age at stroke/moyamoya diagnosis, and moyamoya diagnosis (confirmed or presumed). Two significant associations emerged: a strong positive relationship for children with a moyamoya diagnosis and caregiver-reported BASC-3 anxiety scores; and a strong positive correlation between moyamoya diagnosis and caregiver-reported BRIEF2 BRI total score. Age at revascularization and moyamoya diagnosis significantly predicted BRIEF2 caregiver-reported BRI total scores. These preliminary findings suggest our selected variables may predict caregiver and self-reported behavioural dysregulation and anxiety, requiring early intervention and ongoing support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".