Mitral valve surgery for rheumatic heart disease: replace, repair, retrain?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Rheumatic heart disease (RHD) affects over 30 million people worldwide. Substantial variation exists in the surgical treatment of patients with RHD. Here, we aim to review the surgical techniques to treat RHD with a focus on rheumatic mitral valve (MV) repair. We introduce novel educational paradigms to embrace repair-oriented techniques in cardiac centers. RECENT FINDINGS: Due to the low prevalence of RHD in high-income countries, limited expertise in MV surgery for RHD, technical complexity of MV repair for RHD and concerns about durability, most surgeons elect for MV replacement. However, in some series, MV repair is associated with improved outcomes, fewer reinterventions, and avoidance of anticoagulation-related complications. In low- and middle-income countries, the RHD burden is large and MV repair is more commonly performed due to high rates of loss-to-follow-up and barriers associated with anticoagulation, international normalized ratio monitoring, and risk of reintervention. SUMMARY: Increased consideration for MV repair in the setting of RHD may be warranted, particularly in low- and middle-income countries. We suggest some avenues for increased exposure and training in rheumatic valve surgery through international bilateral partnership models in endemic regions, visiting surgeons from endemic regions, simulation training, and courses by professional societies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".