Evaluation of robotic cardiac surgery simulation training: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To compare the currently available simulation training modalities used to teach robotic surgery. METHODS: Forty surgical trainees completed a standardized robotic 10-cm dissection of the internal thoracic artery and placed 3 sutures of a mitral valve annuloplasty in porcine models and were then randomized to a wet lab, a dry lab, a virtual reality lab, or a control group that received no additional training. All groups trained to a level of proficiency determined by 2 expert robotic cardiac surgeons. All assessments were evaluated using the Global Evaluative Assessment of Robotic Skills in a blinded fashion. RESULTS: Wet lab trainees showed the greatest improvement in time-based scoring and the objective scoring tool compared with the experts (mean, 24.9 ± 1.7 vs 24.9 ± 2.6; P = .704). The virtual reality lab improved their scores and met the level of proficiency set by our experts for all primary outcomes (mean, 24.9 ± 1.7 vs 22.8 ± 3.7; P = .103). Only the control group trainees were not able to meet the expert level of proficiency for both time-based scores and the objective scoring tool (mean, 24.9 ± 1.7 vs 11.0 ± 4.5; P < .001). The average duration of training was shortest for the dry lab and longest for the virtual reality simulation (1.6 hours vs 9.3 hours; P < .001). CONCLUSIONS: We have completed the first randomized controlled trial to objectively compare the different training modalities of robotic surgery. Our data demonstrate the significant benefits of wet lab and virtual reality robotic simulation training and highlight key differences in current training methods. This study can help guide training programs in investing resources in cost-effective, high-yield simulation exercises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".