Quantitative Assessment of Acute Intracranial Clot and Collaterals on High-Resolution Magnetic Resonance Imaging
Bibliographic record
Abstract
INTRODUCTION: There has been an increasing demand for imaging methods that provide a comprehensive evaluation of intracranial clot and collateral circulation, which are helpful for clinical decision-making and predicting functional outcomes. We aimed to quantitatively evaluate acute intracranial clot burden and collaterals on high-resolution magnetic resonance imaging (HR-MRI). METHODS: We analyzed acute ischemic stroke patients with internal carotid artery or middle cerebral artery occlusion in a prospective multicenter study. The clot burden was scored on a scale of 0-10 based on the clot location on HR-MRI. The collateral score was assigned on a scale of 0-3 using the minimum intensity projection from HR-MRI. Uni- and multivariable logistic regression analyses were performed to assess their correlation with clinical outcome (modified Rankin Scale >2 at 90 days). Thresholds were defined to dichotomize into low- and high-score groups, and predictive performances were assessed for clinical and radiologic outcomes. RESULTS: Ninety-nine patients (mean age of 60.77 ± 11.54 years) were included in the analysis. The interobserver correlation was 0.89 (95% CI: 0.77-0.95) for the clot burden score and 0.78 (95% CI: 0.53-0.90) for the collateral score. Multivariable logistic regression analysis demonstrated that the collateral score (odds ratio: 0.41, 95% CI: 0.19-0.90) was significantly associated with clinical outcomes. A better functional outcome was observed in the group with clot burden scores greater than 7 (p = 0.011). A smaller final infarct size and a higher diffusion-weighted imaging-based Alberta Stroke Program Early Computed Tomography Score were observed in the group with collateral scores greater than 1 (all p < 0.05). CONCLUSIONS: HR-MRI offers a new tool for quantitative assessment of clot burden and collaterals simultaneously in future clinical practices and research endeavors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".