Defining the Learning Curve in Minimally Invasive Cardiac Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Minimally invasive cardiac surgery (MICS) has become a popular approach due to its potential benefits, such as improved cosmesis, faster recovery, shorter hospital stays, and cost-effectiveness, compared with traditional median sternotomy. However, there have been some concerns regarding procedural efficiency and surgical outcomes, especially in the early phase of the learning curve of these procedures. METHODS: In March 2025, a systematic review was conducted using MEDLINE, Embase, the Cochrane Library and Google Scholar databases to identify potential studies that quantitively assessed the learning curve in MICS using predefined metrics based on surgical times and/or clinical outcomes. RESULTS: There were 28 studies involving 13,257 patients that met the inclusion criteria, most of which were retrospective, focusing on 3 types of MICS: minimally invasive mitral valve surgery, aortic valve replacement, and coronary artery bypass grafting. The learning curve was assessed using arbitrary (split-group) and nonarbitrary (cumulative sum) methods. Common perioperative metrics included operative, cardiopulmonary bypass, aortic cross-clamp times, and postoperative complications. The reported number of cases needed to overcome the learning curve varied widely, ranging from 23 to 125 cases (mean, 39 cases [for repair] and 78 [for replacement]) for minimally invasive valve surgery, 40 to 138 cases (mean, 93 cases) for minimally invasive aortic valve replacement, and 16 to 100 cases (mean, 40 cases) for minimally invasive coronary artery bypass grafting. CONCLUSIONS: Differences in surgical process and postoperative outcomes suggest a learning curve in MICS, although stable morbidity and mortality rates indicate the safe adoption of these procedures with appropriate training. Nonetheless, significant heterogeneity across studies prevents precise learning curve characterization, highlighting the need for standardized, multivariable assessment frameworks.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".