Ischemic mitral regurgitation: when should one intervene?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Optimal timing of intervention for ischemic mitral regurgitation remains to be elucidated. This review summarizes the data on the management of ischemic mitral regurgitation, and their implications on current practice and future research. RECENT FINDINGS: Mechanistically, ischemic mitral regurgitation can present as Type I, Type IIIb or mixed Type I and IIIb disease. Severity of mitral regurgitation is typically quantified with echocardiography, either transthoracic or transesophageal echocardiography, but may also be assessed via cardiac MRI. In patients with moderate ischemic mitral regurgitation, revascularization can lead to left ventricular reverse remodeling in some. In patients with severe ischemic mitral regurgitation, mitral valve replacement may be associated with fewer adverse events related to heart failure and cardiovascular readmissions, compared with valve repair, although reverse remodeling may be better in patients following successful mitral repair. Transcatheter edge-to-edge repair also further complements the treatment of ischemic mitral regurgitation. SUMMARY: A tailored approach to patients should be considered for each patient presenting with ischemic mitral regurgitation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".