Risk Factors for Death or Stroke After Carotid Endarterectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid endarterectomy is an effective method for preventing strokes if patients do not suffer adverse perioperative outcomes. The purpose of this study was to identify preoperative patient risk factors for adverse outcomes (death or nonfatal stroke) after carotid endarterectomy through the use of a large population-based registry from Ontario, Canada. METHODS: Medical records of all 6038 patients who underwent carotid endarterectomy in Ontario between January 1, 1994, and December 31, 1997, were abstracted from 34 hospitals. Patient characteristics (demographic data, past medical history, neurological symptoms, comorbidities, radiological findings) and 30-day postoperative death or stroke rates were analyzed with logistic regression analysis. RESULTS: The overall 30-day death or stroke rate after surgery was 6.0%. A history of transient ischemic attack or stroke (odds ratio [OR], 1.75; 95% confidence interval [CI], 1.39 to 2.20), atrial fibrillation (OR, 1.89; 95% CI, 1.29 to 2.76), contralateral carotid occlusion (OR, 1.72; 95% C.I., 1.25 to 2.38), congestive heart failure (OR, 1.80; 95% CI, 1.15 to 2.81), and diabetes (OR, 1.28; 95% CI, 1.01 to 1.63) were significant independent predictors for 30-day death or stroke. These 5 factors were combined into a simple risk score that can be used to stratify patients into different risk groups for complications after surgery. CONCLUSIONS: Several patient characteristics predict the development of stroke and death after carotid endarterectomy. These characteristics may help clinicians in patient counseling and contribute to studies "benchmarking" the outcomes of carotid surgery in the community setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".