Selecting conduits for coronary artery bypass grafting: caution regarding the right internal mammary artery
Bibliographic record
Abstract
This narrative review summarizes the angiographic and clinical outcome results of the most common coronary artery bypass grafting (CABG) conduits. The left internal mammary artery is the preferred first conduit to bypass the left anterior descending artery due to superior long-term survival and graft patency. Recent studies suggest the radial artery may be the preferred second conduit for the circumflex or right coronary artery territories, challenging the belief that the right internal mammary artery is the best choice. Despite their historical high failure rates, saphenous vein grafts continue to be widely used as secondary conduits. Several recent studies report suboptimal rates of right internal mammary artery graft failure, with clinical outcomes comparable to or worse than saphenous veins. The suboptimal rates of RIMA graft failure may be attributed to several factors such as improvements in vein graft failure rates, the use of in situ and non-left anterior descending artery grafting configurations, and skeletonized harvesting techniques. While observational studies favor multiple over single arterial grafting, randomized studies are needed for confirmation. The ongoing Randomized comparison of the clinical Outcome of single vs. Multiple Arterial grafts (ROMA) trial aims to determine if multiple arterial grafting reduces major adverse cardiovascular events and mortality and how secondary conduit selection influences these outcomes. Greater adoption of arterial grafting strategies is likely to come from high-quality evidence of benefit and safety from ongoing and future large pragmatic trials.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| 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".