Repair or observe moderate ischemic mitral regurgitation during coronary artery bypass grafting? Prospective randomized multicenter data.
Bibliographic record
Abstract
Ischemic mitral regurgitation (MR) is a common occurrence following myocardial infarction and its presence is associated with poor outcomes. The optimal treatment of ischemic MR is a matter of debate, especially for patients with moderate MR severity. Some authors advocate for isolated coronary artery bypass grafting (CABG) for patients with moderate MR, maintaining that reverse ventricular remodeling will reduce MR grade and its associated mortality risk, while others argue that a concomitant mitral valve repair (MVR) or replacement is superior. The Cardiothoracic Surgical Trials Network (CTSN) recently published the 1-year results of the Surgical Treatment of Moderate Ischemic Mitral Regurgitation study, a multicenter, randomized, controlled trial investigating the impact of MVR in addition to CABG compared to CABG alone in the treatment of moderate ischemic MR. Here, we have reviewed previous observational and prospective studies investigating moderate ischemic MR treatment as well as the results of the current CTSN randomized trial. Furthermore, we have summarized the current state of the available evidence and preview potential new information that will become available with planned subgroup analyses and further follow-up of enrolled patients in the recently completed CTSN trial.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".