Pilot Assessment of Immersive Virtual Reality Renal Models as an Educational and Preoperative Planning Tool for Percutaneous Nephrolithotomy
Bibliographic record
Abstract
BACKGROUND: Percutaneous nephrolithotomy (PCNL) requires the urologist to have detailed knowledge of the stone and its relationship with the renal anatomy. Immersive virtual reality (iVR) provides patient-specific three-dimensional models that might be beneficial in this regard. Our objective is to present the initial experience with iVR in surgeon planning and patient preoperative education for PCNL. MATERIALS AND METHODS: From 2017 to 2018 four surgeons, each of whom had varying expertise in PCNL, used iVR models to acquaint themselves with the renal anatomy before PCNL among 25 patients. iVR renderings were also viewed by patients using the same head-mounted Oculus rift display. Surgeons rated their understanding of the anatomy with CT alone and then after CT+iVR; patients also recorded their experience with iVR. To assess the impact on outcomes, the 25 iVR study patients were compared with 25 retrospective matched-paired non-iVR patients. Student's t-test was used to analyze collected data. RESULTS: iVR improved surgeons' understanding of the optimal calix of entry and the stone's location, size, and orientation (p < 0.01). iVR altered the surgical approach in 10 (40%) cases. Patients strongly agreed that iVR improved their understanding of their stone disease and reduced their preoperative anxiety. In the retrospective matched-paired analysis, the iVR group had a statistically significant decrease in fluoroscopy time and blood loss as well as a trend toward fewer nephrostomy tracts and a higher stone-free rate. CONCLUSIONS: iVR improved urologists' understanding of the renal anatomy and altered the operative approach in 40% of cases. In addition, iVR improved patient comprehension of their surgery. Clinically, iVR had benefits with regard to decreased fluoroscopy time and less blood loss along with a trend toward fewer access tracts and higher stone-free rates.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".