Identification of a T2-hyperintense Perivascular Space in Brain Arteriovenous Malformations
Bibliographic record
Abstract
BACKGROUND/AIM: Brain arteriovenous malformations (AVMs) are vascular malformations characterized by dysmorphic, aberrant vasculature. During previous surgeries of compact nidus brain AVMs (representing the majority of cases), we have observed a "shiny" plane between nidal and perinidal AVM vessels and the surrounding grey and white matter and hypothesized that preoperative neuroimaging of brain AVMs may show a neuroradiological correlate of these intraoperative observations. PATIENTS AND METHODS: We retrospectively reviewed and analyzed multiplanar and multisequence 3-Tesla magnetic resonance (3T MR) imaging in five consecutive brain AVMs with special attention on imaging characteristics of the brain-AVM interface, i.e., the perivascular and perinidal regions. RESULTS: In all five patients, we identified T2-hypertinense perivascular perinidal spaces, which were predominantly observed around the AVM nidus and less prominently around the feeding arteries or draining veins. CONCLUSION: The identification of T2-hypertinense perivascular spaces surrounding brain AVMs on neuroradiological imaging may provide insights into the anatomico-radiological relationships of brain AVMs and the surrounding grey and white matter parenchyma. These findings could have future implications for our understanding of brain AVM biology and may influence neurosurgical approaches to these lesions.
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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 | 0.003 |
| 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 | 0.001 |
| 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".