Optimal medical therapy after coronary artery bypass grafting: a primer for surgeons
Bibliographic record
Abstract
PURPOSE OF REVIEW: After coronary artery bypass grafting (CABG), patients remain at increased risk of cardiovascular events and death. Cardiac surgeons have the opportunity to reduce this risk by optimizing post-CABG patients' medical therapy. RECENT FINDINGS: Recent developments in lipid-lowering, diabetes management, antithrombotic therapy, and anti-inflammatory therapy can significantly improve prognosis in patients with chronic coronary artery disease. PCSK-9 inhibitors should be used in patients with elevated LDL cholesterol despite maximally tolerated statin therapy. Icosapent ethyl should be considered in patients with elevated triglycerides despite maximally tolerated statin therapy. Long-acting GLP-1 receptor agonists or SLGT-2 inhibitors should be used in all post-CABG patients with type 2 diabetes. Intensified antithrombotic therapy with DAPT or DPI reduces MACE (and DPI reduces mortality) in patients with high atherosclerotic burden. Colchicine has not yet been incorporated into guidelines on OMT for stable CAD but it is reasonable to consider using it in high-risk patients. SUMMARY: We review the foundations of optimal medical therapy after CABG, and summarize recent advances with a focus on practical application for the busy cardiac surgeon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".