Carotid Webs and Recurrent Ischemic Strokes in the Era of CT Angiography
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid webs may cause recurrent ischemic stroke. We describe the prevalence, demographics, clinical presentation, imaging features, histopathology, and stroke risk associated with this under-recognized lesion. MATERIALS AND METHODS: A carotid web was defined on CTA as a thin intraluminal filling defect along the posterior wall of the carotid bulb just beyond the carotid bifurcation on oblique sagittal section CTA that was seen as a septum on axial CTA. Using a prospective case series from April 2013 to April 2014, we describe the demographics, spectrum of imaging features on CTA, and histopathology of these carotid webs. From a retrospective analysis of patients at our center from May 2012 to April 2013 who had a baseline head and neck CTA followed by a brain MR imaging within 1-2 days of the CTA, we determine the period prevalence of carotid webs and the prevalence of ipsilateral stroke on imaging. RESULTS: In the prospective series, the mean age was 50 years (range, 41-55 years); 5/7 patients were women. Recurrent stroke was seen in 5/7 (71.4%) patients with the carotid web; time to recurrence ranged from 1 to 97 months. Histopathology suggested a high probability of fibromuscular dysplasia. In the retrospective series, carotid webs were seen in 7/576 patients for a hospital-based-period prevalence of 1.2% (95% CI, 0.4%-2.5%). Two of these 7 patients had acute stroke in the vascular territory of the carotid web. CONCLUSIONS: A carotid web may contribute to recurrent ischemic stroke in patients with no other determined stroke mechanism. Intimal variant fibromuscular dysplasia is the pathologic diagnosis in most cases. The prevalence of carotid web is low, while the optimal management strategy remains unknown.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".