Intra-operative and post-operative management of conduits for coronary artery bypass grafting: a clinical consensus statement of the European Society of Cardiology Working Group on Cardiovascular Surgery and the European Association for Cardio-Thoracic Surgery Coronary Task Force
Bibliographic record
Abstract
The structural and functional integrity of conduits used for coronary artery bypass grafting is critical for graft patency. Disruption of endothelial integrity and endothelial dysfunction are incurred during conduit harvesting subsequent to mechanical or thermal injury and during conduit storage prior to grafting, leading to acute thrombosis and early graft failure. Late graft failure, in particular that of vein grafts, is precipitated by progressive atherogenesis. Intra-operative management includes appropriate selection of conduit-specific harvesting techniques and storage solutions. Arterial grafts are prone to vasospasm subsequent to surgical manipulation, and application of intra-operative vasodilatory protocols is critical. Post-operative management includes continuation of oral vasodilator therapy and selection of antithrombotic and lipid-lowering agents to attenuate atherosclerotic disease progression in conduits. In this review, the scientific evidence underlying the key aspects of intra- and post-operative management of conduits for coronary artery bypass grafting is examined. Clinical consensus statements for best clinical practice are provided, and areas requiring further research are highlighted.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".