P.192 Arterial Hemodynamics and the Clinical Presentation of Cerebral Arteriovenous Malformations
Bibliographic record
Abstract
Background: Arterial Hemodynamics have been implicated in hemorrhage from cerebral arteriovenous malformations (AVMs). The correlation between hemodynamic characteristics and the tendency of AVMs to rupture has been explored in the past, and various theories have been proposed to explain the clinical presentation of AVMs as a hemorrhage vs. seizure. Methods: We monitored feeder artery pressures in 45 patients with AVMS (16 presenting with hemorrhage, 29 without) during super selective angiography and AVM embolization. Results: Mean feeder artery pressure (FP) was found to be 49mm Hg. The mean FP in patients presenting with hemorrhage was somewhat higher than in those without hemorrhage, but the difference was not statistically significant (53.8 mm Hg vs 47.0 mm Hg, p=0.13). Systemic mean pressure was found to correlate with AVM size (r=-0.31, p=0.037). Significant predictors of feeder artery pressure were systemic pressure, AVM size, and the distance of microcatheter from the circle of Willis. Meanwhile, the presence or absence of venous outflow stenosis and the position of the AVM nidus (superficial or deep to the cortical surface) were the most significant predictors of AVM hemorrhage vs seizures. Conclusions: Anatomic factors may be more important than arterial hemodynamic factors in determining the clinical presentation of cerebral AVMs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".