Clinical Outcomes of Endovascular Treatment for Carotid Artery Dissection Without Intracranial Large Vessel Occlusion in Patients With Cerebral Ischemia Presentation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We describe the clinical characteristics and outcomes (including the long-term patency of endovascular treatment [EVT]) of patients with acute ischemic strokes (AISs) featuring carotid artery dissection (CAD) but not intracranial large vessel occlusion. METHODS: We retrospectively reviewed patients who underwent EVT for spontaneous or post-traumatic AISs with CAD over a 13 year period from September 2005 to November 2018. The indications for EVT in patients with AIS-related CAD were a pretreatment diffusion-weighted imaging-Alberta Stroke Program early computed tomography (ASPECT) score > 6 and, clinical-diffusion mismatch. But, if the patients showed fluctuated ischemic symptoms, the joint decision by a stroke neurologist and neurointerventionist was done according to the onset-to-door time, symptoms, patient data, and the initial neuroimaging findings whether indicated that EVT was appropriate. RESULTS: Twenty-two dissected carotid arteries underwent balloon angioplasty and/or stent placement. The patients were 6 women and 16 men of median age 46 years. Twelve lacked any trauma history. Recombinant tissue plasminogen activator was prescribed for two (9.1%) patients. Four developed symptomatic intracranial hemorrhages (18.2%) but 86.4% exhibited modified Rankin scores ≤ 2. CONCLUSIONS: Although attention to the hemorrhagic complication is required, EVT for selective patients with cerebral ischemia associated with CAD may be safe and acceptable treatment strategy for reconstruction of luminal patency, with good clinical outcomes. Prospective large-scale randomized studies are required to optimize EVT for CAD patients.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".