Percutaneous coronary interventions in patients with a previous coronary artery bypass graft surgery
Bibliographic record
Abstract
Coronary artery bypass grafting (CABG) remains the gold standard for revascularization in patients with complex coronary artery disease (CAD), especially those with diabetes mellitus. However, graft failure and progressive native CAD frequently requires repeat revascularization. While percutaneous coronary intervention (PCI) is the most employed secondary strategy, evidence guiding best practice remains limited. This doctorate investigates PCI in patients with prior CABG, examining target vessel selection, procedural characteristics, antithrombotic strategies, and outcomes. We first conducted a literature review of CABG and PCI management in this population. Subsequently, using U.S. registry data, we evaluated clinical outcomes by gender and ethnicity, revealing comparable outcomes post-PCI across groups. A large single-center study in New York and a nationwide registry in the Netherlands assessed outcomes based on PCI target vessels, including native arteries, venous, and arterial grafts, and highlighted the differences in procedural risks and clinical outcomes. Finally, we examined antiplatelet therapy strategies using data from the TWILIGHT trial, showing ticagrelor monotherapy reduced bleeding without compromising ischemic protection in prior CABG patients. Overall, this thesis underscores the heterogeneity and complexity of PCI in prior CABG patients, calls for tailored treatment strategies, and highlights the need for randomized trials to optimize care in this growing yet understudied population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".