Simulating Laparoscopic Renal Hilar Vessel Injuries: Preliminary Evaluation of a Novel Surgical Training Model for Residents
Bibliographic record
Abstract
INTRODUCTION: Many surgical training programs utilize simulation-based strategies for instruction and assessment of laparoscopic skills. While the use of inanimate, animate, and virtual-reality simulation for basic or procedural skills training has been well described, the use of simulation for the purpose of training surgeons in managing intraoperative laparoscopic complications has been given less attention. We describe a novel, affordable inanimate surgical model for use in simulation-based training of laparoscopic renal hilar vessel injury management. METHODS: Using a laparoscopic box trainer, a half-inch Penrose drain, standard silicone intravenous tubing, and a commercially available kidney part-task trainer, an inanimate surgical training model was developed to simulate various clinical scenarios involving renal hilar vessel injuries. To evaluate the construct validity of this training model, urology residents from the University of California, Irvine, completed a simulated scenario involving a renal vein injury (RVI) during laparoscopic radical nephrectomy (LRN). RESULTS: This surgical model is able to simulate both renal arterial and venous injuries during laparoscopic radical and partial nephrectomy scenarios. Initial cost to construct the model was ~800 U.S. dollars (USD) and each subsequent use was an additional 7 USD. Resident training level correlated strongly with technical performance (p<0.01) and "blood loss" (p=0.02) during the "RVI during LRN" scenario. The checklist and global rating scale used to assess performance demonstrated adequate reliability (Cronbach's α=0.82). CONCLUSIONS: While further validation, technical refinement, and improved fidelity are being considered, we present a novel, affordable surgical model for simulating laparoscopic renal hilar vessel injuries that is suitable for urology trainees.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 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".