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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".