Consensus statement—graft treatment in cardiovascular bypass graft surgery
Bibliographic record
Abstract
Coronary artery bypass grafting (CABG) is and continues to be the preferred revascularization strategy in patients with multivessel disease. Graft selection has been shown to influence the outcomes following CABG. During the last almost 60 years saphenous vein grafts (SVG) together with the internal mammary artery have become the standard of care for patients undergoing CABG surgery. While there is little doubt about the benefits, the patency rates are constantly under debate. Despite its acknowledged limitations in terms of long-term patency due to intimal hyperplasia, the saphenous vein is still the most often used graft. Although reendothelialization occurs early postoperatively, the process of intimal hyperplasia remains irreversible. This is due in part to the persistence of high shear forces, the chronic localized inflammatory response, and the partial dysfunctionality of the regenerated endothelium. "No-Touch" harvesting techniques, specific storage solutions, pressure controlled graft flushing and external stenting are important and established methods aiming to overcome the process of intimal hyperplasia at different time levels. Still despite the known evidence these methods are not standard everywhere. The use of arterial grafts is another strategy to address the inferior SVG patency rates and to perform CABG with total arterial revascularization. Composite grafting, pharmacological agents as well as latest minimal invasive techniques aim in the same direction. To give guide and set standards all graft related topics for CABG are presented in this expert opinion document on graft treatment.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".