Comprehensive comparison of carotid endarterectomy primary closure and patch angioplasty: A single-institution experience
Bibliographic record
Abstract
Background: Carotid endarterectomy (CEA) is an effective intervention for the treatment of high-grade carotid stenosis. Technical preferences exist in the operative steps including the use patch for arteriotomy closure. The goals of this study are to compare the rate of postoperative complications and the rate of recurrent stenosis between patients undergoing primary versus patch closure during CEA. Methods: Retrospective chart review was conducted for patients who underwent CEA at single institution. Vascular surgeons mainly performed patch closure technique while neurosurgeons used primary closure. Patients’ baseline characteristics as well as intraprocedural data, periprocedural complications, and postprocedural follow-up outcomes were captured. Results: Seven hundred and thirteen charts were included for review with mean age of 70.5 years (SD = 10.4) and males representing 64.2% of the cohort. About 49% of patients underwent primary closure while 364 (51%) patients underwent patch closure. Severe stenosis was more prevalent in patients receiving patch closure (94.5% vs. 89.4%; P = 0.013). The incidence of overall complications did not differ between the two procedures (odds ratio = 1.23, 95% confidence intervals = 0.82–1.85; P = 0.353) with the most common complications being neck hematoma, strokes, and TIA. Doppler ultrasound imaging at 6 months postoperative follow-up showed evidence of recurrent stenosis in 15.7% of the primary closure patients compared to 16% in patch closure cohort. Conclusion: Both primary closure and patch closure techniques seem to have similar risk profiles and are equally robust techniques to utilize for CEA procedures.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".