Clot composition in retrieved thrombi after mechanical thrombectomy in strokes due to carotid web
Bibliographic record
Abstract
BACKGROUND: The association of carotid webs (CaW) and ischemic stroke is being increasingly recognized. Data on the histologic clot architecture in strokes caused by CaW has not been previously described. Understanding thrombi histopathology may provide insight into the pathophysiology of CaW-related strokes. METHODS: This case series presents three patients with acute ischemic stroke thought to be caused by ipsilateral CaW. Thromboemboli were retrieved from the middle cerebral artery (MCA) by mechanical thrombectomy and histologic analysis was performed. RESULTS: Three patients aged between 41 and 55 years with few to no vascular risk factors presented with symptoms concerning for an acute MCA territory infarction (National Institutes of Health Stroke Scale (NIHSS) range 10-17). Non-contrast computed tomography (CT) Alberta Stroke Program Early CT Score (ASPECTS) range was 7-8 and all patients had hyperdense vessel sign. Initial CT angiogram was concerning for CaW with no superimposed thrombus, later confirmed with conventional angiography. All patients underwent thrombectomy with full reperfusion. Comprehensive stroke workup failed to reveal other etiologies besides ipsilateral CaW. The histopathologic appearance was of typical fresh mixed thrombi. Qualitative thrombus composition analysis of clot from Case #1 yielded 42.5% fibrin, 50.0% red blood cells (RBC), and 7.5% white blood cells (WBC); Case #2 yielded 46.9% fibrin, 43.4% RBC, and 9.7% WBC; and Case #3 yielded 61.5% fibrin, 31.8% RBC, and 6.7% WBC. CONCLUSIONS: The clot composition of large vessel occlusion strokes from CaW is comparable to the histopathology of previously reported clots from other stroke etiologies. Advanced staining techniques may aid in further characterizing the thrombi of this poorly understood condition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".