Does the Brush-Sign Reflect Collateral Status and DWI-ASPECTS in Large Vessel Occlusion?
Bibliographic record
Abstract
Introduction: The relevance of the brush-sign remained poorly documented in large vessel occlusion (LVO). We aimed to assess the relationship between the brush-sign and collateral status and its potential impact on baseline diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) in acute ischemic stroke (AIS) patients eligible to mechanical thrombectomy (MT). Methods: Consecutive patients admitted in the Lyon Stroke Center with anterior circulation AIS due to intracranial internal carotid artery (ICA) and/or M1 or M2 segment of the middle cerebral artery (MCA) occlusion eligible for MT were included. The brush-sign was assessed on T2-gradient-echo MRI. Collateral status was assessed on digital subtraction angiography according to the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) score. Results: = 0.017]. In univariable analysis, a DWI-ASPECTS < 7 was associated with a brush sign. Following multivariable analysis, the brush-sign no longer affected DWI-ASPECTS < 7 while the latter remained associated with younger age [odds ratio (OR) 0.97, 95% CI.96-0.99], male sex (OR 1.79, 95% CI 1.08-2.99), a higher National Institutes of Health Stroke Scale (NIHSS) score (OR 1.16, 95% CI 1.1-1.21), a poor collateral status (OR 9.35, 95% CI 5.59-16.02), MCA segment (OR 2.54, 95% CI 1.25-5.38), and intracranial ICA (OR 3.01, 95% CI 1.16-8) occlusion. Conclusions and Relevance: The brush-sign may be a marker of poor collateral status but did not independently predict a lower DWI-ASPECTS. Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT04620642.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".