The learning curve of robotic cardiac surgery: a scoping review
Bibliographic record
Abstract
This systematic review aims to investigate the learning curve associated with robotic cardiac surgical procedures and its impact on operative efficiency and patient outcomes. An electronic search of MEDLINE, MEDLINE In-Process, Embase, and the Cochrane Library databases was conducted in October 2023. Studies reporting outcomes of robotic cardiac surgical procedures during the early phase of the learning curve process were included. Intraoperative metrics and clinical outcomes were examined. Following the removal of duplicates, 2305 citations were screened, with 32 studies meeting inclusion criteria for full-text screening. Seven studies focused on totally endoscopic coronary artery bypass (TECAB), 12 on robotic mitral valve repair (MVR), and 8 on robotic coronary artery bypass grafting (CABG). Analysis revealed improved procedural efficiency along the learning curve, evidenced by reductions in surgical durations and operative complications. Notable enhancements were observed in total procedure time, bypass time, harvest time, and cross-clamp/occlusion time. Low mortality rates were consistently reported at both 30 days and 1-year post-surgery. As surgeons progress along the learning curve, there is a notable improvement in procedural efficiency and a reduction in adverse events. However, variability in the number of procedures required to attain proficiency suggests the influence of program size and individual surgeon experience. Standardized training protocols and ongoing mentorship are essential to optimize the learning curve while ensuring patient safety. Further research employing standardized metrics to define competency thresholds and expedite the learning process is warranted to enhance the proficiency of robotic cardiac surgeons.
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".