Carotid stenting for symptomatic carotid artery web: Multicenter experience
Bibliographic record
Abstract
ObjectiveCarotid artery webs are an underappreciated cause of recurrent ischemic stroke, and may represent a significant portion of cryptogenic stroke. Evidence-based guidelines for the management of symptomatic carotid webs do not exist. The goal of this study is to audit our local experience for patients with symptomatic carotid artery webs undergoing carotid stenting as a treatment option, along with describing the hypothesized dynamic physiology of carotid webs.MethodsAll patients undergoing stenting for symptomatic carotid artery web at two comprehensive regional stroke centers with high endovascular thrombectomy volume from January 1, 2012 to March 1, 2021 were included. The modified Rankin Scale (mRS) score was used to define functional outcome at 3 months after stenting.ResultsFourteen consecutive patients with symptomatic carotid artery webs underwent stenting. Twelve patients were female (86%), with a median age of 54 (IQR, 48-64) years across all patients. Stroke was the qualifying event in 12 (86%) patients and TIA in 2. Eleven patients (11/14, 79%) achieved a mRS score of 0-2 at 90 days, 2 (14%) were mRS 3-5, and one patient was lost to follow-up. The median follow-up was 12 months (IQR, 10-12). There was no recurrent stroke or TIA like symptoms in any patients.ConclusionsCarotid stenting appears to be safe at preventing recurrent stroke/TIA with a median follow-up of 12 months in this retrospective multicenter observational study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".