Abstract TMP89: Predictive Model of Ischemic Event Recurrence in Pediatric Moyamoya
Bibliographic record
Abstract
Background: Moyamoya is a progressive cerebral arteriopathy and one of the leading causes of stroke recurrence in children. Clinical severity scores have been developed for adult patients given the multifaceted pathophysiology of moyamoya. However, these scores were not designed to assess the risk of ischemic event recurrence, and they have not been validated in children. Objective: To develop a predictive model of ischemic event recurrence adapted to pediatric moyamoya. Methods: We retrospectively reviewed a single-center cohort of moyamoya patients, extracting demographic, clinical, and radiographic data. A binary logit mixed-effects regression was applied to predict ischemic event recurrence or disease progression based on this data. Variables were selected based on current literature. The model was validated using three approaches: validation, leave-one-out cross-validation (LOOCV), and 10-fold cross-validation, assessing performance via area under the curve (AUC), prediction accuracy, and binary classification entropy (BCE). Results: Two-hundred and twenty hemispheres were analyzed. Sixty-six hemispheres showed evidence of ischemic event recurrence or disease progression over a mean follow-up period of 6.23 ± 4.19 years. The median time to event was 21 months. Age at presentation (β=-0.036; p=0.48), Asian ancestry (β=1.74; p=0.002), asymptomatic presentation (β=1.084; p=0.207)), ischemic symptoms at presentation (β=2.15; p=0.011), evidence of infarction on initial brain MRI (β=-0.79; p=0.155), presence of an ivy sign (β=-0.2; p=0.67), unilateral disease (β=-0.97; p=0.077) with an intercept at β=-2.02 (p=0.052) were the variables included in the model. Computed model included LOOCV and 10-fold produced highest prediction accuracy (0.77 and 0.773 respectively), whereas 10-fold cross-validation had the smallest BCE value along with AUC=0.736. Conclusions: We present a pediatric-specific predictive model for the recurrence of ischemic events in moyamoya patients, demonstrating good accuracy. Our performance metrics indicate that 10-fold cross-validation is the preferred method for predicting moyamoya recurrence. This model provides a framework for developing a scoring scale that can be used in clinical practice to quantify risk and guide treatment decisions.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".