The impact of sequential grafting on clinical outcomes following coronary artery bypass grafting☆
Bibliographic record
Abstract
OBJECTIVES: Sequential anastomoses in coronary artery bypass grafting (CABG) offer theoretical advantages including increased graft flow and more complete revascularisation. However, published studies concerning the safety and efficacy of this technique are not definitive. The objective of this study was to assess the effect of sequential anastomoses on outcomes following CABG. METHODS: Perioperative data were prospectively collected on all patients with triple-vessel disease who underwent first-time, isolated, on-pump CABG between 1995 and 2005 at a single centre. Patients with a left internal mammary artery graft to the anterior wall and saphenous vein grafts to the lateral and posterior walls were included. RESULTS: Compared to patients without sequential anastomoses (n=1108), patients with sequential anastomoses (n=1246) were more likely to have an ejection fraction (EF)<40% (14.9% vs 10.8%, p=0.004), a recent myocardial infarction (19.3% vs 14.3%, p=0.001) and an urgent/emergent operative status (19.6% vs 14.4%, p=0.0008). Median follow-up was 78 months. After adjusting for clinical covariates, sequential grafting was not an independent predictor of in-hospital adverse events (odds ratio (OR) 1.15, 95% confidence interval (CI) 0.88-1.50, p=0.31) or long-term mortality and/or readmission to hospital (hazard ratio (HR) 0.98, 95% CI 0.86-1.12, p=0.74). Sequential grafting was an independent predictor of receiving greater than three distal anastomoses (OR 9.26, 95% CI; 6.27-13.67, p<0.0001). CONCLUSIONS: Patients undergoing sequential grafting presented with greater acuity and worse systolic function. After adjusting for baseline differences, sequential grafting was not found to be an independent predictor of adverse events. These results support the safety of sequential anastomoses in patients undergoing CABG.
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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 | 0.007 |
| 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 | 0.001 | 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".