The number of myocardial infarction segments connected to papillary muscle is associated with the improvement in moderate ischemic mitral regurgitation
Bibliographic record
Abstract
Background: We evaluated whether the number of myocardial infarction (MI) segments connected to the papillary muscle (PM), as assessed using cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE), predicts whether moderate ischemic mitral regurgitation (IMR) improves after isolated coronary artery bypass grafting (CABG) to guide the choice of surgical strategy. Methods: A total of 54 patients diagnosed with coronary heart disease (CHD) complicated with moderate IMR who underwent isolated CABG were selected continuously in this retrospective study at Beijing Anzhen Hospital. All patients underwent preoperative LGE. The patients were divided into the IMR improved group (37 patients) and the unimproved group (17 patients) according to 1-year postoperative echocardiography. The factors associated with no IMR improvement after isolated CABG were analyzed. There was no trial registration and no publication of the study protocol. Results: The number of MI segments connected to PM was an independent risk factor for no IMR improvement after isolated CABG [odds ratio 4.39; 95% confidence interval (CI): 1.93–9.98; P<0.001]. The optimal receiver operating characteristic (ROC) curve cut-off value for no IMR improvement was ≥2 (sensitivity: 82.4%; specificity: 83.8%). Follow-up at 1–5 years (median, 2.8 years) showed that the incidences of major adverse cardiovascular and cerebrovascular events (5.4% vs. 23.5%; P=0.041) and New York Heart Association (NYHA) grade (P=0.026) were higher in the unimproved group. Conclusions: In patients with CHD complicated with moderate IMR, the number of MI segments connected to PM is an independent risk factor for no IMR improvement after isolated CABG. Mitral valve surgery should be performed simultaneously with CABG in patients with ≥2 MI segments connected to the PM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".