White matter hyperintensity burden and collateral circulation in acute ischemic stroke with large artery occlusion
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the association between white matter hyperintensity (WMH) burden and pial collaterals in acute strokes caused by intracranial large artery occlusion treated with mechanical thrombectomy in the anterior circulation, focusing on stroke subtypes. METHODS: Consecutive patients undergoing mechanical thrombectomy between December 2019 and June 2022 were retrospectively screened. The Fazekas scale assessed WMH burden. Pial collaterals were categorized as either poor (0-2) or good (3-4) based on the Higashida score. A multivariable analysis was used to determine the relationship between WMH burden and pial collaterals. Subgroup analyses delved into associations stratified by stroke subtypes, namely cardioembolism (CE), tandem lesions (TLs), and intracranial atherosclerosis (ICAS). RESULTS: Of the 573 patients included, 274 (47.8%) demonstrated poor pial collaterals. Multivariable regression indicated a strong association between extensive WMH burden (Fazekas score of 3-6) and poor collaterals [adjusted OR 3.04, 95% CI 1.70-5.46, P < 0.001]. Additional independent predictors of poor collaterals encompassed ICAS-related occlusion (aOR 0.26, 95% CI 0.09-0.76, P = 0.014), female sex (aOR 0.63, 95% CI 0.41-0.96, P = 0.031), and baseline Alberta Stroke Program Early Computed Tomography scores (aOR 0.80, 95% CI 0.74-0.88, P < 0.001). Notably, an interaction between extensive WMH burden and stroke subtypes was observed in predicting poor collaterals (P = 0.001), being pronounced for CE (adjusted OR 2.30, 95% CI 1.21-4.37) and TLs (adjusted OR 5.09, 95% CI 2.32-11.16), but was absent in ICAS (adjusted OR 1.24, 95% CI 0.65-2.36). CONCLUSIONS: Among patients treated with mechanical thrombectomy for anterior circulation large artery occlusion, extensive WMH burden correlates with poor pial collaterals in embolic occlusion cases (CE and TLs), but not in ICAS-related occlusion.
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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".