International multicenter cohort study of pediatric brain arteriovenous malformations. Part 1: Predictors of hemorrhagic presentation
Bibliographic record
Abstract
OBJECTIVE Brain arteriovenous malformations (AVMs) are the most common cause of spontaneous intracranial hemorrhage in pediatric patients (age < 18 years). Since the cumulative lifetime risk of AVM hemorrhage is considerable in children, an improved understanding of the risk factors influencing hemorrhagic presentation may aid in the management of pediatric AVMs. The aims of this first of a 2-part multicenter, retrospective cohort study are to evaluate the incidence and determine the predictors of hemorrhagic presentation in pediatric AVM patients. METHODS The authors analyzed pooled AVM radiosurgery data from 7 institutions participating in the International Gamma Knife Research Foundation (IGKRF). Patients younger than 18 years at the time of radiosurgery and who had at least 12 months of follow-up were included in the study cohort. Patient and AVM characteristics were compared between unruptured and ruptured pediatric AVMs. RESULTS A total of 357 pediatric patients were eligible for analysis, including 112 patients in the unruptured and 245 patients in the ruptured AVM cohorts (69% incidence of hemorrhagic presentation). The annual hemorrhage rate prior to radiosurgery was 6.3%. Hemorrhagic presentation was significantly more common in deep locations (basal ganglia, thalamus, and brainstem) than in cortical locations (frontal, temporal, parietal, and occipital lobes) (76% vs 62%, p = 0.006). Among the factors found to be significantly associated with hemorrhagic presentation in the multivariate logistic regression analysis, deep venous drainage (OR 3.2, p < 0.001) was the strongest independent predictor, followed by female sex (OR 1.7, p = 0.042) and smaller AVM volume (OR 1.1, p < 0.001). CONCLUSIONS Unruptured and ruptured pediatric AVMs have significantly different patient and nidal features. Pediatric AVM patients who possess 1 or more of these high-risk features may be candidates for relatively more aggressive management strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".