P.098 Primary closure versus expansile patch angioplasty for carotid endarterectomy: a single center series
Bibliographic record
Abstract
Background: Carotid endarterectomy (CEA) is a common treatment option for patients presenting with carotid stenosis; however, the optimal method for arterial closure remains unclear. Therefore, we examined our single center series to compare primary closure versus patch angioplasty for carotid endarterectomy. Methods: We reviewed all patients who underwent CEA from 2008 to 2016. Closure method was entirely based on the surgeon style (i.e., all patients treated by vascular surgeons underwent patch angioplasty and all individuals managed by neurosurgeons undergone primary closure). Data were reported as frequencies and outcomes as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Results: A total of 713 patients were included (349 in the primary closure group and 364 in the patch group). Underlying baseline characteristics were similar between both groups. The risk of transient ischemic attack (OR, 7.08; 95%CI, 0.41-2.84; P=0.872), stroke (OR, 1.14; 95%CI, 0.58-2.22; P=0.697), myocardial infarction (OR, 1.10; 95% CI, 0.39-3.07; P=0.851), cranial nerve palsy (OR, 1.79; 95%CI, 0.65-4.91; P=0.248), and post-operative neck hematoma (OR, 1.04; 95%CI, 0.48-2.24; P=0.923) didn’t differ significantly between the two closure options. Conclusions: Our findings suggest that primary closure and expansile angioplasty have similar safety and efficacy profiles as treatment closure options among patients undergoing CEA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".