PD08-02 IMMERSIVE VIRTUAL-REALITY FOR PERCUTANOUS NEPHROLITHOTOMY: IMPACT ON PATIENT EDUCATION, SURGICAL PLANNING AND TREATMENT OUTCOME
Bibliographic record
Abstract
You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (PD08)1 Apr 2019PD08-02 IMMERSIVE VIRTUAL-REALITY FOR PERCUTANOUS NEPHROLITHOTOMY: IMPACT ON PATIENT EDUCATION, SURGICAL PLANNING AND TREATMENT OUTCOME Egor Parkhomenko*, Mitchell O'Leary, Shoaib Safiullah, Francis Jefferson, Sartaaj Walia, Ryan James, Cyrus Lin, Roshan Patel, Kamaljot Kaler, Jaime Landman, and Ralph Clayman Egor Parkhomenko*Egor Parkhomenko* More articles by this author , Mitchell O'LearyMitchell O'Leary More articles by this author , Shoaib SafiullahShoaib Safiullah More articles by this author , Francis JeffersonFrancis Jefferson More articles by this author , Sartaaj WaliaSartaaj Walia More articles by this author , Ryan JamesRyan James More articles by this author , Cyrus LinCyrus Lin More articles by this author , Roshan PatelRoshan Patel More articles by this author , Kamaljot KalerKamaljot Kaler More articles by this author , Jaime LandmanJaime Landman More articles by this author , and Ralph ClaymanRalph Clayman More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555247.96110.71AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Percutaneous nephrolithotomy (PCNL) requires urologists to have detailed knowledge of the stone and its relationship to the renal anatomy. Immersive virtual reality (iVR) provides patient-specific 3D models that might be beneficial in this regard. Our objective is to evaluate the impact of iVR on surgeon's preoperative planning, clinical outcomes, and patient education. METHODS: Four endourologists used iVR models (Figure 1) to acquaint themselves with the renal anatomy prior to PCNL in 25 patients. iVR renderings were also viewed by patients using the same head-mounted Oculus Rift display (Facebook Inc.). Using a Likert-type scale (1=strongly disagree to 5=strongly agree), surgeons rated their understanding of the anatomy after viewing CT images only and then after the iVR experience; using a similar Likert-type scale, patients recorded their experience with iVR. Next, iVR patients were matched with 25 retrospective non-iVR patients with similar age, ASA, BMI, stone burden, gender, and nephrostomy tract location. Student's t-test (Excel) was used for data analysis. RESULTS: iVR improved surgeons' understanding of the optimal calyx of entry and the stone's location, size/orientation (p<0.01) (Table 1). iVR altered the surgical approach in 10 (40%) cases. Patients strongly agreed that iVR reduced their preoperative anxiety (p<0.05). In the retrospective matched-paired analysis, the iVR group had a significant decrease in fluoroscopy time (139 vs. 269 sec, p=0.027) and blood loss (66 vs. 206 mL, p=0.019) as well as a trend toward fewer nephrostomy needle passes (1.13 vs. 1.46 passes; p=0.10) and a higher 100% stone-free rate (9/25 vs 5/25, p=0.15). CONCLUSIONS: iVR prior to PCNL improved urologists' understanding of the renal anatomy, altered the operative approach, and mitigated patients' preoperative anxiety. Clinically, iVR decreased both fluoroscopy time and blood loss and trended toward fewer access tracts and higher stone free rates. Source of Funding: none Boston, MA; Orange, CA; Colombia, MO; Orange, CA; Seattle, WA; Orange, CA; Calgary, Canada; Orange, CA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e148-e148 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Egor Parkhomenko* More articles by this author Mitchell O'Leary More articles by this author Shoaib Safiullah More articles by this author Francis Jefferson More articles by this author Sartaaj Walia More articles by this author Ryan James More articles by this author Cyrus Lin More articles by this author Roshan Patel More articles by this author Kamaljot Kaler More articles by this author Jaime Landman More articles by this author Ralph Clayman More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".