The journey to improving stroke risk stratification for patients with carotid artery stenosis: from medical management to revascularization
Bibliographic record
Abstract
Ischemic strokes remain a leading cause of mortality and morbidity worldwide. Carotid artery stenosis is a major risk factor for ischemic strokes. Although traditional carotid revascularization procedures are based on carotid stenosis, it has been increasingly recognized that plaque composition plays an important role in plaque rupture and stroke occurrence. Our narrative review aims to present the evidence related to 1) carotid atherosclerosis and plaque composition contributors to stroke and 2) medical management and revascularization of patients with carotid artery stenosis for stroke prevention. For patients with severe carotid atherosclerosis, critical treatment modalities include best medical therapy and revascularization, specifically, carotid endarterectomy (CEA) or carotid artery stenting (CAS) for symptomatic and asymptomatic individuals, according to stenosis guidelines (≥50% and ≥70% stenosis, respectively). Landmark randomized controlled trials (RCTs) showcased the clinical value of surgery in reducing future stroke outcomes for asymptomatic and symptomatic populations. Along with the latest advancements in medical therapy, results from modern RCTs are providing much needed evidence regarding the net benefits in revascularization for stroke risk reduction, namely, in asymptomatic populations. Evidence suggests that carotid artery stenosis, the primary metric for CEA or CAS eligibility is not always consistent with the degree of plaque instability. We emphasize the importance of combining plaque instability and carotid stenosis assessments to better classify at-risk patients. Along with integrations of interventions with modern medical treatment, novel findings from RCTs and consideration of stenosis and plaque instability will ultimately help improve individualized care leading to effective prevention of ischemic strokes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".