Revascularization in chronic coronary syndrome: shifts in clinical practice guidelines
Bibliographic record
Abstract
PURPOSE OF REVIEW: The optimal revascularization strategy for chronic coronary syndrome (CCS) is rapidly evolving due to emerging evidence and technological advancements. This review will discuss the guidelines for the management of CCS, examining how they align with and respond to recent high-quality studies. We will also discuss the evolution of the guidelines and highlight key differences. RECENT FINDINGS: While broad consensus exists between the most recent European and American guidelines, notable differences exist in the management of multivessel disease with preserved ejection fraction and left main disease. The role of the Heart Teams has become increasingly vital particularly when the guidelines do not fit the clinical scenarios, and the evidence is controversial. SUMMARY: Determining the optimal management strategy for patients with CCS requires careful consideration of anatomic complexity, comorbidities, and individual patient preferences. While advances in percutaneous coronary intervention (PCI) and medical therapy have been widely discussed, it is equally important to contextualize these with emerging innovations in surgical revascularization. Techniques such as multiple arterial grafting and minimally invasive surgical approaches represent significant progress in coronary artery bypass surgery. Randomized trials that compare state-of-the-art percutaneous and surgical revascularization techniques are thus needed.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".