Learning Curve Analysis of Minimally Invasive Mitral Valve Repair
Bibliographic record
Abstract
OBJECTIVE: Previous learning curve analyses of minimally invasive mitral valve (MV) repair have focused largely on early safety outcomes without including detailed mitral repair quality outcomes. This study investigates the learning curve of minimally invasive MV repair over a 15-year experience, focused on clinical outcomes and evidence-based technical failure endpoints. METHODS: All MV repair operations were performed by a single surgeon between May 2008 and February 2023. Patient data were stratified into 3 groups of tertiles. Failure endpoints were defined as postrepair residual mitral regurgitation ≥ mild and a 30-day composite outcome. Cumulative log-likelihood curves were constructed for minimally invasive MV repair using the primary outcomes as technical failure endpoints. Control limits were determined using previous analyses of the Society of Thoracic Surgeons database. RESULTS: = 0.005). Learning curve analysis demonstrated crossing of the lower threshold at ~60 patients for postrepair mitral regurgitation ≥ mild and ~85 patients for the 30-day composite outcome. The mean adjusted risk scores for both primary outcomes based on a multivariable logistic model demonstrated no significant differences across tertiles. CONCLUSIONS: The estimated number of operations to achieve optimal repair outcomes and durability is ~60 to 85 patients. These data can improve the design of surgical training competencies, beyond avoidance of complications, and instead focus the learning curve on what is necessary to achieve optimal mitral repair outcomes.
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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 | 0.010 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".