Diagnostic and Management Strategies in Patients with Late Recurrent Angina after Coronary Artery Bypass Grafting
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will outline the current evidence on the anatomical, functional, and physiological tools that may be applied in the evaluation of patients with late recurrent angina after coronary artery bypass grafting (CABG). Furthermore, we discuss management strategies and propose an algorithm to guide decision-making for this complex patient population. RECENT FINDINGS: Patients with prior CABG often present with late recurrent angina as a result of bypass graft failure and progression of native coronary artery disease (CAD). These patients are generally older, have a higher prevalence of comorbidities, and more complex atherosclerotic lesion morphology compared to CABG-naïve patients. In addition, guideline recommendations are based on studies in which post-CABG patients have been largely excluded. Several invasive and non-invasive diagnostic tools are currently available to assess graft patency, the hemodynamic significance of native CAD progression, left ventricular function, and myocardial viability. Such tools, in particular the latest generation coronary computed tomography angiography, are part of a systematic diagnostic work-up to guide optimal repeat revascularization strategy in patients presenting with late recurrent angina after CABG.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".