Perioperative Ischemic Complications of the Brain After Carotid Endarterectomy
Bibliographic record
Abstract
BACKGROUND: The potential morbidity of cerebral ischemia after carotid endarterectomy (CEA) has been recognized, but its reported incidence varies widely. OBJECTIVE: To prospectively evaluate the development of cerebral ischemic complications in patients treated by CEA at a high-volume cerebrovascular center. METHODS: Fifty patients with moderate or severe carotid stenosis awaiting CEA were studied with perioperative diffusion-weighted imaging of the brain and standardized neurological evaluations. Microsurgical CEA was performed by 1 of 2 vascular neurosurgeons. Radiological studies were evaluated by faculty neuroradiologists who were blinded to the details of the clinical situation. RESULTS: Preoperative diffusion-weighted imaging studies were performed within 24 hours of surgery. A second study was obtained within 24 (92% of patients), 48 (4% of patients), or 72 (4% of patients) hours after surgery. Intraluminal shunting was used in 1 patient (2%), and patch angioplasty was used in 2 patients (4%). No patient had diffusion-weighted imaging evidence of procedure-related cerebral ischemia. Nonischemic complications consisted of postoperative confusion in an 87-year-old man with a urinary tract infection and a marginal mandibular nerve paresis in another patient. Radiological studies were normal in both patients. CONCLUSION: CEA is a relatively safe procedure that may be performed with an acceptable risk of cerebral ischemia in select patients. The low rate of ischemic complications associated with CEA sets a standard to which other carotid revascularization techniques should be held. The current results are presented with a discussion of the senior author's preferred surgical technique and a brief review of the literature.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".