Etiologic and prognostic value of external carotid artery thrombus detection during endovascular therapy for anterior circulation proximal occlusions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: An early understanding of stroke mechanism may improve treatment and outcome in patients presenting with large vessel occlusion stroke (LVOS) treated with mechanical thrombectomy (MT). We aimed to investigate whether spontaneous external carotid artery (ECA) embolism detection during MT is associated with stroke etiology and clinical outcome. METHODS: We retrospectively reviewed our prospectively maintained institutional database including consecutive patients with anterior circulation LVOS treated with MT between January 2015 and August 2020. RESULTS: An ECA embolus was detected in 68 of 1298 patients (5.2%). The kappa coefficient for interobserver agreement was 0.89 (95% confidence interval [CI] 0.82-0.95). ECA embolism was significantly associated with intracranial internal carotid artery (ICA) occlusion (p < 0.001), cardioembolic etiology (p < 0.001) and a lower clot burden score (p < 0.001). Day-1 variation of National Institutes of Health Stroke Scale score (adjusted odds ratio [OR] -2.7, 95% CI -4.9 to 0.3; p = 0.021) and delta Alberta Stroke Program Early Computed Tomography Score (adjusted OR 0.9, 95% CI 0.2 to 1.5; p = 0.004) were worse among patients with ECA emboli. There was no significant difference in 90-day functional outcome between groups (adjusted OR 0.8, 95% CI 0.42 to 1.52; p = 0.50). CONCLUSION: In patients with anterior circulation LVOS treated with MT, ECA embolism was significantly associated with cardioembolic etiology, high thrombus burden and proximal intracranial ICA occlusions. This underexplored angiographic pattern might provide a valuable etiologic clue to the underlying cause of anterior circulation LVOS and may also help determine the appropriate revascularization strategy.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".