RANDOMIZED CONTROLLED TRIAL COMPARING THE EFFICACY OF IMMERSIVE VIRTUAL REALITY AND TRADITIONAL PHYSICAL ARTHROSCOPY SIMULATORS IN ARTHROSCOPIC TRAINING
Bibliographic record
Abstract
Surgical simulation using virtual reality (VR) technology is a novel, risk-free modality for developing the technical skills of surgical trainees. This study aimed to compare the development of arthroscopic skills following training with an immersive VR arthroscopy simulator to a physical arthroscopy simulator. Eligible participants were randomized into one of four groups: VR3 (three VR training sessions), VR1 (one VR training session), physical simulator, and no training (Figure 1). Participants were medical students with no previous arthroscopy experience. Training metrics and testing performance were evaluated using the global rating scale (GRS) scoring system and standardized rater assessments. Qualitative questionnaires were administered to assess participant experiences and perceptions of the simulators. Out of 38 initial respondents, 29 participants met eligibility criteria and were randomized. The training metrics analysis showed that the VR3 training group exhibited significantly better performance over time compared to the VR1 group. Intragroup analysis within the VR3 group revealed improvements in precision, rotating, periscoping, and object tracking skills from first session to last session. The physical simulator group had significantly better GRS scores in terms of instrumental and camera dexterity compared to the no training group (Figure 2). No other significant differences were found among the four groups in all other components of the GRS. VR3 had a shorter task completion time compared to the VR1 and no training groups. Change in confidence levels did not vary significantly among the groups. The repeated use of the physical arthroscopy simulator was perceived to provide additional benefit and continued learning compared to the VR groups (p=0.039). Participants reported high levels of enjoyment, learning, and understanding across all groups. Strengths and limitations of both the IVR and physical simulators were identified based on participant feedback. The findings of this study suggest that non-anatomic VR and physical arthroscopy simulators provide similar training benefits, with physical arthroscopy simulators demonstrating slightly more favorable outcomes in some training metrics. However, VR, may be a viable option for arthroscopic training due to its cost-effectiveness and portability. These findings highlight the potential of VR in surgical education. For any figures or tables, please contact the authors directly.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".