Developing an <i>Ex Vivo</i> Pig Model for Teaching Ultrasound and Fluoroscopy-Guided Percutaneous Renal Access
Bibliographic record
Abstract
Introduction: Establishing percutaneous renal access is the key initial step to percutaneous nephrolithotomy; however, learning the technique during surgery for trainees is complicated by the number of approaches used to gain access, limited completion time during a breath hold. and attempt to minimize the number of passes through a kidney. There are many training models for percutaneous access commercially available all with their respective limitations. Our objective was to develop a low-cost, high-fidelity percutaneous access training model that addresses existing limitations and can be used with both ultrasound and fluoroscopy guidance. Methods: After a formal ethics exemption was attained, pig cadavers were harvested for flank, kidneys, and ureters. These were incorporated into a composite porcine tissue mould, created within a gelatin matrix. In the initial assessment, establishing percutaneous access under both ultrasound and fluoroscopy guidance was tested to refine usability. Once acceptable, its use during a training course was evaluated to assess impressions for use with ultrasound. Results: We were able to create a $45USD biodegradable model, which can facilitate percutaneous access using: fluoroscopy with intrarenal contrast; fluoroscopy with endoscopic guidance; and fluoro-less that is, ultrasound only. A cohort of 12 Canadian Postgraduate Year-3 residents who used the model for ultrasound access agreed that the model simulated a comparable tactile experience (58.33%) and anatomy (75%) to humans. Furthermore, majority of the residents agreed that model was easy to use with ultrasound guidance (91.67%), was a beneficial experience for their learning and future practice (83.33%) and if available would use to complement their intraoperative training (83.33%). Conclusion: We were able to develop a low-cost, preliminarily tested ex vivo pig model for percutaneous access compatible with multiple imaging modalities. We will continue refining our model and seek to understand its benefits when teaching percutaneous access to varying levels of learners.
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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 | 0.001 |
| 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) | 0.004 | 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".