Symptomatic carotid near-occlusion causes a high risk of recurrent ipsilateral ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of recurrent ipsilateral ischemic stroke in patients with symptomatic near-occlusion with and without full collapse. METHODS: Included were consecutive patients eligible for revascularization, grouped into symptomatic conventional ≥ 50% carotid stenosis (n = 266), near-occlusion without full collapse (n = 57) and near-occlusion with full collapse (n = 42). The risk of preoperative recurrent ipsilateral ischemic stroke was analyzed, or, for cases not revascularized within 90 days, 90-day risk was analyzed. RESULTS: The risk of a preoperative recurrent ipsilateral ischemic stroke or ipsilateral retinal artery occlusion was 15% (95% CI 9-20%) for conventional ≥ 50% stenosis, 22% (95% CI 6-38%) among near-occlusion without full collapse and 30% (95% CI 16-44%) among near-occlusion with full collapse (p = 0.01, log rank test). In multivariate analysis, near-occlusion with full collapse had a higher risk of recurrent ipsilateral ischemic stroke (adjusted HR 2.6, 95% CI 1.3-5.3) and near-occlusion without full collapse tended to have a higher risk (adjusted HR 2.0, 95% CI 0.9-4.5) than conventional ≥ 50% stenosis. Only 24% of near-occlusion with full collapse underwent revascularization, common causes for abstaining were misdiagnosis as occlusion (31%), deemed surgically unfeasible (21%) and low perceived benefit (10%). CONCLUSIONS: Symptomatic carotid near-occlusion has a high short-term risk of recurrent ipsilateral ischemic stroke, especially near-occlusion with full collapse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".