Carotid Artery Angioplasty and Stenting: Introduction of a New Technique Into an Established Vascular Surgery Center
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to review our initial experience with the introduction of carotid artery angioplasty and stenting as a treatment for carotid stenosis in high-risk patients and compare clinical outcomes to carotid endarterectomy patients treated over the same time period at our center. METHODS: A total of 265 carotid revascularization procedures (45 carotid artery angioplasty and stenting and 220 carotid endarterectomy) were performed over 3 years period. In the carotid artery angioplasty and stenting group, 93% were at high risk according to the current reporting standards. Death, neurological events, and restenosis rates were compared at 30 days and at most recent follow-up. RESULTS: Mean follow-up for all patients was 18 months (range 0-48 months). Carotid artery angioplasty and stenting group had higher cardiac risk than carotid endarterectomy group (13% vs 2%, P < .05). High-risk carotid lesions were present in 67% of carotid artery angioplasty and stenting patients. There was a tendency toward higher restenosis rate in carotid artery angioplasty and stenting than in carotid endarterectomy patients (35% vs 15%, P = .06). Combined stroke and death was higher in the carotid stenting group (4% and 9%) compared to the carotid endarterectomy group (0.5% and 0.5%) at 30 days and at late follow-up, respectively (P = .04 and .00). CONCLUSION: Restenosis and stroke were observed more frequently in our initial experience in patients undergoing carotid artery angioplasty and stenting compared with carotid endarterectomy patients during the same time period. These differences disappeared in high-risk patients. Further studies, to evaluate the effect of the learning curve on early results as well as follow-up for intermediate and long-term durability of carotid artery angioplasty and stenting in high-risk patients, are required.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".