Abstract P316: Non-Stenotic Carotid Plaques in Ischemic Stroke - Analysis of the STRATIS Registry
Bibliographic record
Abstract
Objective: To determine the prevalence of non-stenotic carotid plaques (<50%) and their association with ipsilateral strokes. Methods: Data was analyzed from the STRATIS registry (Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke)—a prospective, nonrandomized study of patients undergoing thrombectomy with the Solitaire device. Prevalence of non-stenotic carotid plaques, ipsilateral and contralateral to the stroke was compared in patients with ESUS and cardioembolic strokes. Plaque features were further compared within both subgroups between patients with and without ipsilateral stroke. Uni- and multivariable logistic regression was performed to determine associations between non-stenotic carotid plaque, plaque characteristics, and ipsilateral stroke in both subgroups. Results: Of the 946 patients in the database, 226 patients with cardioembolic stroke (median age, 72 years) and 141 patients with ESUS (median age, 69 years) were included in the analysis.The prevalence of non-stenotic carotid plaque in the cardioembolic and ESUS subgroups was 33/226 (14.6%) and 32/141(22.7%) respectively. Bilateral non-stenotic carotid plaques were seen in 10/226(4.4%) cardioembolic and 13/141(9.2%) ESUS patients. Non-stenotic carotid plaques were significantly associated with ipsilateral strokes in cardioembolic stroke (aOR,1.91 [95% CI,1.15-3.18]) and in ESUS (aOR,1.69 [95% CI, 1.05-2.73]). Plaque irregularity, plaque hypodensity and increasing plaque thickness were significantly associated with ipsilateral stroke, only in the ESUS subgroup. Conclusion: Non-stenotic carotid plaques were significantly associated with ipsilateral stroke in cardioembolic and ESUS subgroups and there was increased association of hypodense plaque, increasing plaque thickness and plaque irregularity with ipsilateral stroke in the ESUS subgroup, suggesting these plaques could be a potential cause of stroke in these patient subgroups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".