Simulation-Based Percutaneous Renal Access Training: Evaluating a Novel 3D Immersive Virtual Reality Platform
Bibliographic record
Abstract
Introduction: Percutaneous nephrolithotomy (PCNL) is the gold standard treatment for patients with a large stone burden. There are a variety of methods to teach this important endourologic procedure, including simulation. We evaluated three different PCNL simulation platforms for potential use in teaching and assessing percutaneous renal access skills. Materials and Methods: Urology residents, fellows, and faculty were recruited to participate in this study, which included completing standardized tasks on three PCNL simulation platforms: a virtual reality (VR) simulator (PercMentor, 3D Systems™), a porcine tissue simulator (Cook™ Medical), and a new 3D immersive VR simulator—Marion K181 (Marion Surgical™). Participants were asked to complete a standardized task—gaining prone percutaneous renal access using a fluoroscopic-guided technique. Participants were asked to rate the simulators, and performance data were recorded for analysis. Results: A total of 18 participants with varying levels of PCNL experience completed the study. The Marion K181 had higher ratings by participants in all domains (realism, tactile feedback, instrument movement, renal anatomy, fidelity of simulation, utility as teaching tool) compared with the PercMentor (p < 0.05) but did not differ in any domain when compared with the porcine PCNL model. Participants felt that the Marion K181 was comparable with the porcine PCNL model as a teaching tool, but had the advantage of not requiring radiation exposure. Fluoroscopy time was the variable that most consistently correlated with participant PCNL experience and level of training, across all three PCNL simulation platforms. Conclusions: There are a variety of PCNL simulation platforms available for teaching percutaneous renal access skills. Based on our initial comparative study, there is validity evidence to support the use of the novel Marion K181 PCNL simulator as a training tool rather than higher fidelity models requiring real radiation exposure. However, evidence is yet lacking for its use as an assessment tool.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".