Primary Stenting for Recurrent Stenosis Following Carotid Endarterectomy
Bibliographic record
Abstract
BACKGROUND: Carotid angioplasty and stenting is an accepted alternative treatment for severe restenosis following carotid endarterectomy. Balloons may not be required to effectively treat these lesions, given their altered histopathology compared to primary atherosclerotic plaque and tendency to be less calcified. Primary stenting using self-expanding stents alone may, therefore, be a safe and effective treatment for restenosis post-carotid endarterectomy. METHODS: We review our experience in the treatment of 12 patients with symptomatic severe restenosis following carotid endarterectomy with primary stent placement alone. RESULTS: Self-expanding stent placement alone reduced the mean internal carotid artery stenosis from 85% to 29%. Average peak systolic velocity determined at the time of ultrasonography decreased from 480 cm/s at initial presentation to 154 cm/s post-stent deployment and further decreased to 104 cm/s at one year follow-up. The stented arteries remained widely patent with no evidence of restenosis. A single peri-procedural ipsilateral transient ischemic event occurred. There were no cerebral or cardiac ischemic events recorded at one year of follow-up. CONCLUSIONS: In this series, primary stent placement without use of angioplasty balloons was a safe and effective treatment for symptomatic restenosis following carotid endarterectomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".