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Training in percutaneous nephrolithotomy

Authors: Shashikant, Mishra; Jitendra, Jagtap; Ravindra B, Sabnis; Mahesh R, Desai;

Training in percutaneous nephrolithotomy

Abstract

Training in percutaneous nephrolithotomy (PCNL) necessitates the trainee to climb the steep learning curve of this procedure sequentially. The initial steps of the process should be the acquisition of the necessary skills in a nonintimidating skills lab. We review the current scenario of the training in PCNL and advocate the means that may improve the overall patient care.The training involves a comprehensive development of the trainee. Initial process starts with the cognitive skills update through conferences and observing peers do the procedure. Rapid prototyping could be useful for resident education. The benefits of three-dimensional stereolithographic biomodeling produced from computed tomography data may aid in achieving optimal access. Skills lab involving wet and dry lab reinforce the cognitive skills. The advantage of live anesthetized porcine model is it being a more realistic model and assessment tool. The specific advantage of the dry lab simulator is of repetitive tasking and easier setup feasibility. There is a lack of guideline for the lab setup and training. Funding, location, number of models installed, curriculum, a trained mentor, and instructor are the critical components that need to be planned in advance.Training in PCNL starts with cognitive knowledge, reinforcement through repetitive nonpatient basic skills acquisition in wet and dry skills lab, prototyping the technique before the actual procedure, and finally supervised training under an able mentor.

Related Organizations
Keywords

Kidney Calculi, Models, Animal, Animals, Humans, Urologic Surgical Procedures, Computer Simulation, Clinical Competence, Curriculum, Nephrostomy, Percutaneous

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
31
Top 10%
Top 10%
Top 10%
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