Bibliographic record
Abstract
In recent years, coronary artery bypass grafting (CABG) has been reconfirmed as bringing significant advantages in survival and freedom from adverse cardiovascular outcomes over percutaneous coronary interventions (PCIs). In the post-Synergy between PCI with Taxus and Cardiac Surgery and The Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease trials era, it had initially been thought that the next generation of coronary artery stents would bring comparatively better outcomes for PCI. However, the modern Everolimus-Eluting Stents or Bypass Surgery for Left Main Coronary Artery Disease and Nordic-Baltic-British left main revascularisation study trials showed inferior outcomes, including poorer survival, with PCI compared to CABG [1]. As such, a resurgence of CABG is being observed in several countries, including the United States and Canada. For cardiovascular practitioners, cardiologists, and surgeons, this is a call and opportunity to make the CABG operation even better for our present and future patients. To this end, this section of Current Opinion in Cardiology presents seminal topics to the reader, including how conduits can be optimized to make the CABG operation potentially curative (an article led by Dr Mario Gaudino – HCO340607); what CABG may look like in the future (as foreseen by Professor Falk's team – HCO340610); and how the manner and even decision to perform CABG is influenced by renal failure and myocardial dysfunction, two vexing comorbid conditions that are encountered in many of our patients (with articles led by Dr Sun – HCO340608 and Dr Toeg – HCO340617). We hope, dear reader, that you will find this Coronary Artery Surgery section particularly interesting and thought-provoking. Happy reading! Acknowledgements None. Financial support and sponsorship None. Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.224 | 0.130 |
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".