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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".