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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".