Hemorrhaging laparoscopic partial nephrectomy — feasibility of a novel simulation model
Bibliographic record
Abstract
INTRODUCTION: Intraoperative surgical complications pose significant potential risks to patients. Uncontrolled bleeding during laparoscopic partial nephrectomy is one such event that requires collaboration and communication between surgical team members. We developed and evaluated a multidisciplinary surgical simulation scenario and model of intraoperative hemorrhage during a laparoscopic partial nephrectomy to facilitate the practice of these crucial non-technical skills. METHODS: A simulation scenario using a novel, titratable, bleeding partial nephrectomy model was developed. The operating room simulation consisted of an intubated mannequin placed in the lateral decubitus position and laparoscopic renal model. The multidisciplinary simulation scenario included anesthesia and urology residents and progressed from bleeding to a pulseless electrical activity arrest. The degree of renal model bleeding was modified based on the progression of the urology resident. After the scenario, participants were debriefed and completed a post-simulation survey assessing: 1) their perception of the simulated scenario; and 2) their teaching of non-technical skills in their residency training. RESULTS: The porcine model was successfully reproduced for nine consecutive weeks and functioned well to simulate bleeding from a laparoscopic partial nephrectomy site; the bleeding was able to be titrated based on resident progression and excision of the simulated tumor. All residents stated the scenario was valuable to assess and improve non-technical surgical skills and that their exposure to practice non-technical skills in their existing curriculum could be improved. CONCLUSIONS: Simulating an intraoperative bleeding partial nephrectomy, combined with an intraoperative crisis scenario, is a feasible, immersive, and reproducible model and can challenge residents' non-technical skills.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".