Predictors of early and late stroke following cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Much is known about the short-term risks of stroke following cardiac surgery. We examined the rate and predictors of long-term stroke in a cohort of patients who underwent cardiac surgery. METHODS: We obtained linked data for patients who underwent cardiac surgery in the province of Ontario between 1996 and 2006. We analyzed the incidence of stroke and death up to 2 years postoperatively. RESULTS: Of 108,711 patients, 1.8% (95% confidence interval [CI] 1.7%-1.9%) had a stroke perioperatively, and 3.6% (95% CI 3.5%-3.7%) had a stroke within the ensuing 2 years. The strongest predictors of both early and late stroke were advanced age (≥ 65 year; adjusted hazard ratio [HR] for all stroke 1.9, 95% CI 1.8-2.0), a history of stroke or transient ischemic attack (adjusted HR 2.1, 95% CI 1.9-2.3), peripheral vascular disease (adjusted HR 1.6, 95% CI 1.5-1.7), combined coronary bypass grafting and valve surgery (adjusted HR 1.7, 95% CI 1.5-1.8) and valve surgery alone (adjusted HR 1.4, 95% CI 1.2-1.5). Preoperative need for dialysis (adjusted odds ratio [OR] 2.1, 95% CI 1.6-2.8) and new-onset postoperative atrial fibrillation (adjusted OR 1.5, 95% CI 1.3-1.6) were predictors of only early stroke. A CHADS2 score of 2 or higher was associated with an increased risk of stroke or death compared with a score of 0 or 1 (19.9% v. 9.3% among patients with a history of atrial fibrillation, 16.8% v. 7.8% among those with new-onset postoperative atrial fibrillation and 14.8% v. 5.8% among those without this condition). INTERPRETATION: Patients who had cardiac surgery were at highest risk of stroke in the early postoperative period and had continued risk over the ensuing 2 years, with similar risk factors over these periods. New-onset postoperative atrial fibrillation was a predictor of only early stroke. The CHADS2 score predicted stroke risk among patients with and without atrial fibrillation.
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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 | 0.002 |
| 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) | 0.001 | 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".