Preoperative Stroke and Outcomes after Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
BACKGROUND: Data are lacking on the optimal scheduling of coronary artery bypass grafting (CABG) surgery after stroke. The authors investigated the preoperative predictors of adverse outcomes in patients undergoing CABG, with a focus on the importance of the time interval between prior stroke and CABG. METHODS: The Hospital Episode Statistics database (April 2006-March 2010) was analyzed for elective admissions for CABG. Independent preoperative patient factors influencing length of stay, postoperative stroke, and mortality, were identified by logistic regression and presented as adjusted odds ratios (OR). RESULTS: In all, 62,104 patients underwent CABG (1.8% mortality). Prior stroke influenced mortality (OR 2.20 [95% CI 1.47-3.29]), postoperative stroke (OR 1.99 [1.39-2.85]), and prolonged length of stay (OR 1.31 [1.11-1.56]). The time interval between stroke and CABG did not influence mortality or prolonged length of stay. However, a longer time interval between stroke and CABG surgery was associated with a small increase in risk of postoperative stroke (OR per month elapsed 1.02 [1.00-1.04]; P = 0.047). An interaction was evident between prior stroke and myocardial infarction for death (OR 5.50 [2.84-10.8], indicating the importance of the combination of comorbidities. Prominent effects on mortality were also exerted by liver disease (OR 20.8 [15.18-28.51]) and renal failure (OR 4.59 [3.85-5.46]). CONCLUSIONS: The authors found no evidence that more recent preoperative stroke predisposed patients undergoing CABG surgery to suffer postoperative stroke, death, or prolonged length of stay. The combination of prior stroke and myocardial infarction substantially increased perioperative risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".