Reconstruction Technique Options for Achieving Total Arterial Revascularization and Multiple Arterial Grafting
Bibliographic record
Abstract
Ischemic heart disease is the leading cause of morbidity and mortality worldwide and may require coronary revascularization when more severe or symptomatic. Coronary artery bypass grafting (CABG) is the most common cardiac surgical procedure and can be performed with different bypass conduits and anastomotic techniques. Saphenous vein grafts (SVGs) are the most frequently used conduits for CABG, in addition to the left internal thoracic artery. Outcomes with a single internal thoracic artery and SVGs are favorable, and the long-term patency of SVGs may be improved through novel harvesting techniques, preservation methods, and optimal medical therapy. However, increasing evidence points towards the superiority of arterial grafts, especially in the form of multiple arterial grafting (MAG). Nevertheless, the uptake of MAG remains limited and variable, both as a result of technical complexity and a scarcity of conclusive randomized controlled trial evidence. Here, we present an overview of CABG techniques, harvesting methods, and anastomosis types to achieve total arterial revascularization and adopt MAG. We further narratively summarize the available evidence for MAG versus single arterial grafting to date and highlight remaining gaps and questions that require further study to elucidate the role of MAG in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".