Preoperative Virtual Reality Surgical Rehearsal of Renal Access during Percutaneous Nephrolithotomy: A Pilot Study
Bibliographic record
Abstract
Percutaneous Nephrolithotomy (PCNL) is a procedure used to treat kidney stones. In PCNL, a needle punctures the kidney through an incision in a patient’s back and thin tools are threaded through the incision to gain access to kidney stones for removal. Despite being one of the main endoscopic procedures for managing kidney stones, PCNL remains a difficult procedure to learn with a long and steep learning curve. Virtual reality simulation with haptic feedback is emerging as a new method for PCNL training. It offers benefits for both novices and experienced surgeons. In the first case, novices can practice and gain kidney access in a variety of simulation scenarios without offering any risk to patients. In the second case, surgeons can use the simulator for preoperative surgical rehearsal. This paper proposes the first preliminary study of PCNL surgical rehearsal using the Marion Surgical PCNL simulator. Preoperative CT scans of a patient scheduled to undergo PCNL are used in the simulator to create a 3D model of the renal system. An experienced surgeon then planned and practiced the procedure in the simulator before performing the surgery in the operating room. This is the first study involving survival rehearsal using a combination of VR and haptic feedback in PCNL before surgery. Preliminary results confirm that surgical rehearsal using a combination of virtual reality and haptic feedback strongly affects decision making during the procedure.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".