Arterial grafting and complete revascularization
Bibliographic record
Abstract
PURPOSE OF REVIEW: Arterial grafting is superior to venous grafting in coronary artery bypass graft surgery with respect to graft patency and long-term patient outcome, but it may be difficult to achieve complete arterial revascularization. RECENT FINDINGS: Use of arterial grafts, especially bilateral internal mammary artery grafts, is not common, whereas there are clear indications that it may increase survival. Definitions of complete revascularization are varied and confusing, making study comparisons difficult. Technical challenges in complete revascularization with arterial grafts can be minimized by surgical techniques. Competitive flow in moderately stenosed coronary arteries grafted with arterial conduits may result in reduced patency. While internal mammary arteries may be used in arteries with at least 60% stenosis, radial artery and gastroepiploic grafts are best placed onto coronaries with severe stenosis. Moderate lesions in the left coronary circulation should be bypassed, but right coronary artery lesions can be left untouched as there is minimal progression over time. Complete revascularization may not be necessary or possible in every patient because of technical challenges. CONCLUSION: Complete revascularization with arterial grafts presents both technical and physiological challenges. However, with techniques to maximize length of arterial conduits, knowledge of competitive flow and which moderate lesions should be addressed, complete revascularization with arterial grafts can be accomplished in the majority of patients, notwithstanding it may not be possible or even indicated for every patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".