Impaired peri-nidal cerebrovascular reserve in seizure patients with brain arteriovenous malformations
Bibliographic record
Abstract
Epileptic seizures are a common presentation in patients with newly diagnosed brain arteriovenous malformations, but the pathophysiological mechanisms causing the seizures remain poorly understood. We used magnetic resonance imaging-based quantitative cerebrovascular reactivity mapping and conventional angiography to determine whether seizure-prone patients with brain arteriovenous malformations exhibit impaired cerebrovascular reserve or morphological angiographic features predictive of seizures. Twenty consecutive patients with untreated brain arteriovenous malformations were recruited (10 with and 10 without epileptic seizures) along with 12 age-matched healthy controls. Blood oxygen level-dependent MRI was performed while applying iso-oxic step changes in end-tidal partial pressure of CO(2) to obtain quantitative cerebrovascular reactivity measurements. The brain arteriovenous malformation morphology was evaluated by angiography, to determine to what extent limitations of arterial blood supply or the presence of restricted venous outflow and tissue congestion correlated with seizure susceptibility. Only patients with seizures exhibited impaired peri-nidal cerebrovascular reactivity by magnetic resonance imaging (0.11 ± 0.10 versus 0.25 ± 0.07, respectively; P < 0.001) and venous drainage patterns suggestive of tissue congestion on angiography. However, cerebrovascular reactivity changes were not of a magnitude suggestive of arterial steal, and were probably compatible with venous congestion in aetiology. Our findings demonstrate a strong association between impaired peri-nidal cerebrovascular reserve and epileptic seizure presentation in patients with brain arteriovenous malformation. The impaired cerebrovascular reserve may be associated with venous congestion. Quantitative measurements of cerebrovascular reactivity using blood oxygen level-dependent MRI appear to correlate with seizure susceptibility in patients with brain arteriovenous malformation.
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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) | 0.001 | 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".