An In Vivo, MRI-Integrated Real-Time Model of Active Contrast Extravasation in Acute Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The "spot sign" or contrast extravasation is strongly associated with hematoma formation and growth. An animal model of contrast extravasation is important to test existing and novel therapeutic interventions to inform present and future clinical studies. The purpose of this study was to create an animal model of contrast extravasation in acute intracerebral hemorrhage. MATERIALS AND METHODS: Twenty-eight hemispheres of Yorkshire male swine were insonated with an MR imaging-guided focused sonography system following lipid microsphere infusion and mean arterial pressure elevation. The rate of contrast leakage was quantified by using dynamic contrast-enhanced MR imaging and was classified as contrast extravasation or postcontrast leakage by using postcontrast T1. Hematoma volume was measured on gradient recalled-echo MR imaging performed 2 hours postprocedure. Following this procedure, sacrificed brain was subjected to histopathologic examination. Power level, burst length, and blood pressure elevation were correlated with leakage rate, hematoma size, and vessel abnormality extent. RESULTS: Median (intracerebral hemorrhage) contrast extravasation leakage was higher than postcontrast leakage (11.3; 6.3-23.2 versus 2.4; 1.1-3.1 mL/min/100 g; P<.001). Increasing burst length, gradient recalled-echo hematoma (ρ=0.54; 95% CI, 0.2-0.8; P=.007), and permeability were correlated (ρ=0.55; 95% CI, 0.1-0.8; P=.02). Median permeability (P=.02), gradient recalled-echo hematoma (P=.02), and dynamic contrast-enhanced volumes (P=.02) were greater at 1000 ms than at 10 ms. Within each burst-length subgroup, incremental contrast leakage was seen with mean arterial pressure elevation (ρ=0.2-0.8). CONCLUSIONS: We describe a novel MR imaging-integrated real-time swine intracerebral hemorrhage model of acute hematoma growth and contrast extravasation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".