Robotic mitral valve repair: a European single-centre experience†
Bibliographic record
Abstract
OBJECTIVES: We report the outcomes of robotic valve repair for degenerative mitral regurgitation (MR) in our Institution. METHODS: Between February 2012 and July 2016, 134 patients underwent robotic mitral valve (MV) repair with the da Vinci Si system. All the operations were performed through a mini-thoracotomy in the fourth right intercostal space, cardiopulmonary bypass and mild hypothermia. The clinical and echocardiographic follow-up was 100% complete. RESULTS: There was no hospital death. The mean cross-clamp and cardiopulmonary bypass time were 112±23 and 159±33 min, respectively. Pre-discharge echocardiograms showed none-to-mild residual MR in all patients. Median follow-up was 24.1 months. We observed 1 early and 4 late reoperations on the MV for an overall freedom from reoperation of 98.2% and 94.1% at 12 and 36 months, respectively. Furthermore, echocardiographic follow-up revealed freedom from recurrence of MR greater than Grade 1+ of 92.5% and 80.7% at 12 and 36 months, respectively. Nevertheless freedom from recurrence of MR greater than Grade 2+ was 97.2% at 12 and 36 months. CONCLUSIONS: Robotic MV repair is a feasible and safe option for the treatment of degenerative MR in selected patients with excellent perioperative outcomes. Early and midterm results are remarkable and are associated with low risk of late recurrence of MR and reoperation. Long-term follow-up is needed to confirm the durability of valve repair.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".