Introduction of an ex-vivo pig model for teaching percutaneous nephrolithotomy access techniques
Bibliographic record
Abstract
INTRODUCTION: In North America, obtaining access for percutaneous nephrolithotomy (PCNL) is not often performed by urologists. Hands-on training sessions help to ensure this skill continues within the urological community. An ex-vivo pig kidney model was developed for simulation. This model uses porcine tissues with a fluoroscopic C-arm and standard PCNL equipment. The bullseye or triangulation techniques are both possible. We propose this as a high-fidelity tool for teaching PCNL access. METHODS: The pig kidney, fat, ribs, flank, and skin were arranged anatomically on a table with fluoroscopy. Hands-on training was provided to residents and urologists using the ex-vivo pig model and a silicone-based percutaneous access model. Questionnaires were given at the end of the session. RESULTS: There was a total 14 responders for each model, with incomplete responses on two surveys. A total of 15% of responders for the pig model and 7% of responders for the silicone model had previous percutaneous access experience. For the pig model, 93% of trainees agreed or strongly agreed that the model was easy to use, and 79% of the silicone model trainees felt the same. After the session, 50% of silicone model trainees and 86% of pig model trainees reported increased confidence in their ability to obtain PCNL access. All the pig model trainees and 71% of the silicone model trainees felt that the simulation activity was worthwhile. CONCLUSIONS: The inexpensive but anatomically realistic ex-vivo pig model using real-world equipment provides trainees with an excellent tool to learn PCNL access.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".