Presentation and outcomes of mitral valve surgery in France in the recent era: a nationwide perspective
Bibliographic record
Abstract
OBJECTIVES: Unbiased information regarding the surgical management of patients with mitral regurgitation (MR) at the nationwide level are scarce and mainly US-based. The Programme de Médicalisation des Systèmes d'Information, a mandatory national database, offers the unique opportunity to assess the presentation and outcomes of all consecutive mitral valve (MV) surgeries performed in France in the contemporary era. METHODS: We collected all MV surgeries performed for MR in France in 2014-2016. MR aetiology was classified as degenerative (DMR), secondary (SMR) or Other (rheumatic or congenital disease and infective endocarditis). RESULTS: During the 3-year period, 18 167 MV surgeries were performed in France (55% repair and 45% replacement; 52% isolated). Age was 66±12 years and 59% were male. Aetiology was DMR in 42%, SMR in 16% and other in 42% including 19% with uncertain aetiologies. Overall, in-hospital mortality was 6.5% and increased with age, female gender, Charlson Comorbidity Index, type of surgery (replacement vs repair), associated surgery (combined vs isolated) and MR aetiology (all p<0.01). In-hospital mortality and rate of death/readmission for heart failure (HF) at 1 year were 3.4% and 13%, respectively for DMR (2.4% and 11% for isolated DMR) and 7.8% and 27%, respectively for SMR (5.5% and 23% for isolated SMR). Repair rate was 55% overall, 68% in DMR and 72% for isolated DMR surgery (70% of all DMR). Repair rates decreased with age, Charlson Comorbidity Index and female sex (all p<0.0001). CONCLUSION: In this cross-sectional contemporary prospective nationwide database, in-hospital mortality and 1 year rate of death and HF readmission were considerable overall and in all subsets. Repair rates were suboptimal overall especially in the elderly and women subsets. These results underline the need to develop strategies to improve management and outcomes of patients with both DMR and SMR.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".