Which renal access technique for percutaneous nephrolithotomy is more difficult to teach using simulation in surgical training?
Bibliographic record
Abstract
INTRODUCTION: Percutaneous nephrolithotomy (PCNL) is a challenging procedure that urology trainees should be familiar with during residency. Simulators, such as the PERC Mentor, allow the development of this competency in a safer, stress-free environment. There are two primary fluoroscopic methods of gaining percutaneous renal access: the triangulation method and the bull's eye method. Our goal was to assess which method is easier to teach novices by using the PERC Mentor simulator. A secondary goal was to assess differences in subjective and objective outcomes. METHODS: Fifteen simulator and procedure-naive medical trainees were randomized into two groups using a crossover, randomized study design. Participants were provided with written, video, in-person demonstrations and hands-on practice for each technique. They then performed each method and were assessed objectively using the PERC Mentor performance data report and subjectively using the PCNL global rating scale (GRS) scoring system. Statistical analysis was performed using Student's T-test and non-parametric Wilcoxon signed rank test. RESULTS: There was no statistical difference in the outcomes and complication rates between the two methods. The bull's eye method of obtaining percutaneous access was associated with a significant decrease in operative time (91 seconds vs. 128 seconds, p=0.03) and fluoroscopy time (87 seconds vs. 123 seconds, p=0.03) compared to the triangulation method. CONCLUSIONS: Teaching of both techniques was equally well acquired by students. Both techniques had similar outcomes; however, the bull's eye method was associated with less operative and fluoroscopy time when compared to the triangulation method among novices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".