Evaluation of a Virtual Reality Percutaneous Nephrolithotomy (PCNL) Surgical Simulator
Bibliographic record
Abstract
Percutaneous Nephrolithotomy is the standard surgical procedure used to remove large kidney stones. PCNL procedures have a steep learning curve; a physician needs to complete between 36 and 60 procedures, to achieve clinical proficiency. Marion Surgical K181 is a virtual reality surgical simulator, which emulates the PCNL procedures without compromising the well-being of patients. The simulator uses a VR headset to place a user in a realistic and immersive operating theater, and haptic force-feedback robots to render physical interactions between surgical tools and the virtual patient. The simulator has two modules for two different aspects of PCNL kidney stone removal procedure: kidney access module where the user must insert a needle into the kidney of the patient, and a kidney stone removal module where the user removes the individual stones from the organ. In this paper, we present user trials to validate the face and construct validity of the simulator. The results, based on the data gathered from 4 groups of users independently, indicate that Marion's surgical simulator is a useful tool for teaching and practicing PCNL procedures. The kidney stone removal module of the simulator has proven construct validity by identifying the skill level of different users based on their tool path. We plan to continue evaluating the simulator with a larger sample of users to reinforce our findings.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".