Simulation for Percutaneous Renal Access: Where Are We?
Bibliographic record
Abstract
OBJECTIVES: Percutaneous renal access (PCA) is a challenging step during percutaneous nephrolithotomy. The aim of this study is to review the literature for different types of simulators described for PCA. METHODS: Databases of Medline, Embase, Cochrane Library, OvidSP, and Google Scholar were systematically searched until May 2016. The studies were analyzed regarding the type of simulator (nonbiologic, biologic, live animal, and virtual reality [VR]), type of validity (face, content, construct, and predictive), cost-effectiveness, and whether these simulators have been used for training and/or assessment of PCA. In addition, the study looked at the educational impact of these simulators in terms of the transfer of PCA skills to the operating room. RESULTS: Several bench, animal, and VR simulators for training in PCA were identified. Only few studies were found on assessment of PCA skills. Biological bench models used porcine or bovine kidneys wrapped within foam, silicone, chicken carcass, or full-thickness skin flap alone. Other biological models used additional subcutaneous fascia, muscle, or ribs. Nonbiological models used prototypes, including 3D printing. Only one study reported the use of anesthetized live pig for training. The PERC Mentor™ was the only VR simulator, which has been validated for training and assessment of PCA skills. However, none of these studies assessed the educational impact of PCA simulators. Furthermore, most of the studies did not address the validity and the cost of the simulator. CONCLUSIONS: While several biological and nonbiological PCA models exist, there is paucity of literature regarding the validity and educational impact of these simulators. The PERC Mentor simulator is the sole validated simulator for training and assessment of PCA skills. However, it is expensive and there is little evidence of its educational impact. Therefore, more research is needed to validate the available simulators and assess their educational impact 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".