CABG: The Gold Standard or Is There Room to Excel?
Bibliographic record
Abstract
Filmed at the 2020 STS Annual Meeting in New Orleans, Louisiana, Faisal Ghazi Bakaeen of the Cleveland Clinic in Ohio, USA, moderates a discussion on coronary artery bypass grafting (CABG). Dr Bakaeen is joined by Joseph F. Sabik of the University Hospitals Cleveland Medical Center in Ohio, USA; Michael E. Farkouh of the University of Toronto in Canada; Joanna Chikwe of Cedars-Sinai Smidt Heart Institute in Los Angeles, California; and Mario F. L. Gaudino of Weill Cornell Medicine in New York City. They discuss the controversies surrounding the EXCEL trial, other recent trials comparing CABG vs. percutaneous coronary intervention, and ways to improve CABG, with a specific emphasis on the use of multiarterial grafting.DisclosuresDr Sabik serves on the Cardiac Surgery Advisory Board for Medtronic.Dr Farkouh has received research grants from Amgen, Novartis, and Novo Nordisk.Dr Chikwe serves as speaker, consultant, and course organizer for Edwards Lifesciences and Medtronic.DisclaimerThe information and views presented on CTSNet.org represent the views of the authors and contributors of the material and not of CTSNet. Please review our full disclaimer page here.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.129 | 0.049 |
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".