Outcomes after carotid revascularization for symptomatic carotid artery web: A multi-institutional cohort study
Bibliographic record
Abstract
ObjectiveCarotid web can serve as a nidus for clot formation and subsequent thromboembolic stroke. Although treatment historically involved antiplatelet therapy, recent evidence demonstrates notably elevated recurrent stroke rates despite best medical therapy. We examined the safety and efficacy of carotid artery stenting (CAS), which involves placing a coil in the artery, and carotid endarterectomy (CEA), a surgery to remove the clot, for treatment of symptomatic carotid web.MethodsA multi-institutional retrospective registry including adult patients with carotid web with history of ipsilateral acute ischemic stroke or transient ischemic attack (TIA) treated with either carotid artery stenting or endarterectomy was reviewed. Patient demographics, comorbidities, clinical presentation, treatment methodology, and outcomes at follow up were collected.ResultsAmong the 71 included patients (mean age 52.4 years, 44 (62.4%) female), 23 (32.4%) underwent CEA, and 48 (67.6%) underwent CAS. The most common presenting symptoms were hemibody numbness/weakness, aphasia, and gaze deviation. On average, patients had experienced 1.12 ± 0.7 strokes or TIAs before intervention, with a mean delay of 115 ± 224 days between initial stroke/TIA and intervention. The median preintervention modified Rankin Scale (mRS) score was 2. Perioperatively, one patient (1.41%) experienced stroke and none experienced myocardial infarction. During mean 14.8 (±29.2)-month follow up, one patient experienced asymptomatic recurrent stroke (1.41%). Median mRS at follow up was 1, and carotid artery stenting and endarterectomy demonstrated similar rates of postoperative complication and recurrent stroke rates.ConclusionOur results suggest that endarterectomy and stenting are safe and efficacious for minimizing subsequent stroke risk in patients who have experienced stroke or TIA from carotid webbing, warranting further studies to determine the optimal timing and method of treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".