High Fidelity Simulation Based Team Training in Urology: A Preliminary Interdisciplinary Study of Technical and Nontechnical Skills in Laparoscopic Complications Management
Bibliographic record
Abstract
PURPOSE: Simulation based team training provides an opportunity to develop interdisciplinary communication skills and address potential medical errors in a high fidelity, low stakes environment. We evaluated the implementation of a novel simulation based team training scenario and assessed the technical and nontechnical performance of urology and anesthesiology residents. MATERIALS AND METHODS: Urology residents were randomly paired with anesthesiology residents to participate in a simulation based team training scenario involving the management of 2 scripted critical events during laparoscopic radical nephrectomy, including the vasovagal response to pneumoperitoneum and renal vein injury during hilar dissection. A novel kidney surgical model and a high fidelity mannequin simulator were used for the simulation. A debriefing session followed each simulation based team training scenario. Assessments of technical and nontechnical performance were made using task specific checklists and global rating scales. RESULTS: A total of 16 residents participated, of whom 94% rated the simulation based team training scenario as useful for communication skill training. Also, 88% of urology residents believed that the kidney surgical model was useful for technical skill training. Urology resident training level correlated with technical performance (p=0.004) and blood loss during renal vein injury management (p=0.022) but not with nontechnical performance. Anesthesia resident training level correlated with nontechnical performance (p=0.036). Urology residents consistently rated themselves higher on nontechnical performance than did faculty (p=0.033). Anesthesia residents did not differ in the self-assessment of nontechnical performance compared to faculty assessments. CONCLUSIONS: Residents rated the simulation based team training scenario as useful for interdisciplinary communication skill training. Urology resident training level correlated with technical performance but not with nontechnical performance. Urology residents consistently overestimated their nontechnical performance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".