Risk Factors For Recurrent Stroke After Coronary Artery Bypass Grafting
Bibliographic record
Abstract
OBJECTIVES: Preventing stroke after coronary artery bypass grafting (CABG) remains a therapeutic goal, due in part to the lack of identifiable risk factors. The aim of this study, accordingly, was to identify risk factors in CABG patients with a previous history of stroke. METHODS: Patients with a history of stroke who underwent CABG at Beijing An Zhen hospital from January 2007 to July 2010 were selected (n = 430), and divided into two groups according to the occurrence of postoperative stroke. Pre-operative and post-operative data were retrospectively collected and analyzed by univariate and multivariate logistic regression analyses. RESULTS: Thirty-two patients (7.4%) suffered post-operative stroke. Univariate analysis identified several statistically significant risk factors in the post-operative stroke group, including pre-surgical left ventricular ejection fractions (LVEF) ≤50%, on-pump surgery, post-operative atrial fibrillation (AF), and hypotension. Multivariable analysis identified 4 independent risk factors for recurrent stroke: unstable angina (odds ratio (OR) = 2.95, 95% CI: 1.05-8.28), LVEF ≤50% (OR = 2.77, 95% CI: 1.23-6.27), AF (OR = 4.69, 95% CI: 1.89-11.63), and hypotension (OR = 2.55, 95% CI: 1.07-6.04). CONCLUSION: Unstable angina, LVEF ≤50%, post-operative AF, and post-operative hypotension are independent risk factors of recurrent stroke in CABG patients with a previous history of stroke.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".