Validation of a Novel Virtual Reality Training Curriculum for Robotic Cardiac Surgery a Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: Robotic cardiac surgery training has relied entirely on classical methods of surgical teaching. We sought to evaluate the impact of a virtual reality (VR) simulation curriculum to improve skill acquisition in robotic cardiac surgery. METHODS: We randomly assigned 20 surgical trainees to undergo a 9-exercise VR curriculum on a robotic surgical simulator or a control group that received no additional training. The trainees were then evaluated in a blinded fashion by assessing their de-identified video recordings of the following: (1) standardized robotic internal thoracic artery harvest and (2) mitral valve annuloplasty performed in porcine models, using a validated time-based scoring system and an objective intraoperative scoring tool. Postintervention assessments were compared to baseline. RESULTS: Trainees randomized to the VR group were faster than the control group for both the internal thoracic artery harvest (957.3 ± 98.9 vs. 749.1 ± 171.9; P = 0.004) and mitral annuloplasty (580.4 ± 14.4 vs. 463.8 ± 86.4; P < 0.001) and scored significantly higher with the intraoperative scoring tool (22.8 ± 2.7 vs. 11.0 ± 4.5; P < 0.001). Additionally, the VR group achieved a proficiency level similar to our experts for both time-based scores (P = 0.624 and P = 0.967), and the intraoperative assessment (P = 0.110), whereas the control group was not able to meet this level of proficiency for any of the primary outcomes. The average duration of training to successfully complete all required tasks was 9.3 hours. CONCLUSIONS: We have demonstrated that a VR simulation curriculum can significantly improve the efficiency and quality of learning in robotic cardiac surgery. Further evaluation of this curriculum is required for its widespread implementation in surgical training (ClinicalTrials.gov, NCT#02357056).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".