Symptomatic Non-stenotic Carotid Disease in Embolic Stroke of Undetermined Source
Bibliographic record
Abstract
PURPOSE: Non-stenotic (< 50%) carotid disease may play an important etiological role in ischemic stroke classified as embolic stroke of undetermined source (ESUS). We aimed to assess the prevalence of non-stenotic carotid disease and its association with ipsilateral ischemic stroke. METHODS: Data are from ESCAPE-NA1, a randomized controlled trial investigating the neuroprotectant nerinetide in patients with acute ischemic stroke and large vessel occlusion (LVO). The degree of stenosis of the extracranial internal carotid artery (ICA) and high-risk plaque features were assessed on baseline computed tomography (CT) angiography. We evaluated the association of non-stenotic carotid disease and ipsilateral stroke by age-adjusted and sex-adjusted logistic regression and calculated the attributable risk of ipsilateral stroke caused by non-stenotic carotid disease. RESULTS: After excluding patients with non-assessable imaging, symptomatic > 50% carotid stenosis and extracranial dissection, 799/1105 (72.1%) patients enrolled in ESCAPE-NA1 remained for this analysis. Of these, 127 (15.9%) were classified as ESUS. Non-stenotic carotid disease occurred in 34/127 ESUS patients (26.8%) and was associated with the presence of ipsilateral ischemic stroke (odds ratio, OR 1.6, 95% confidence interval, CI 1.0-2.6, p = 0.049). The risk of ipsilateral ischemic stroke attributable to non-stenotic carotid disease in ESUS was estimated to be 19.7% (95% CI -5.7% to 39%), the population attributable risk was calculated as 4.3%. Imaging features such as plaque thickness, plaque irregularity or plaque ulceration were not different between non-stenotic carotids with vs. without ipsilateral stroke. CONCLUSION: Non-stenotic carotid disease frequently occurs in patients classified as ESUS and is associated with ipsilateral ischemic stroke. Our findings support the role of non-stenotic carotid disease as stroke etiology in ESUS, but further prospective research is needed to prove a causal relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".