Secondary prevention after CABG: do new agents change the paradigm?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Coronary artery bypass graft (CABG) surgery remains the gold-standard treatment for multivessel and left main coronary artery disease. Despite significant improvement in cardiovascular outcomes, patients undergoing CABG remain at risk for recurrent adverse ischemic events and other cardiovascular outcomes (coronary revascularisation, stroke, cardiac death, etc.). The purpose of this review is to summarize the most recent evidence in pharmacological preventive therapies addressing the residual cardiovascular risk in patients who have undergone CABG. RECENT FINDINGS: Novel cardiovascular pharmacological preventive strategies targeting inflammatory, metabolic and prothrombotic (antiplatelet and anticoagulation) pathways have been recently assessed, with promising results for secondary prevention after CABG. SUMMARY: Secondary prevention is an essential part of postoperative care after CABG. Novel lipid-lowering and glucose-controlling agents suggest a strong and consistent benefit on native coronary artery disease and overall cardiovascular outcomes. The role and the choice of enhanced antiplatelet/anticoagulation/lipid/glucose-modulating therapies following CABG should be better defined and deserves further investigation. Additional studies are required to identify new therapeutic target addressing the specific multifactorial nature of the graft CV disease and identifying the best preventive strategies for long-term graft patency.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".