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Learning Curves in Pediatric Robot-Assisted Pyeloplasty: A Systematic Review

Journal

JOURNAL OF CLINICAL MEDICINE
Volume 11, Issue 23, Pages -

Publisher

MDPI
DOI: 10.3390/jcm11236935

Keywords

robot-assisted surgery; surgical learning; learning curve; pediatric urology; pyeloplasty

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The learning process of pediatric robot-assisted pyeloplasty (pRALP) requires a certain number of surgical cases to reach proficiency. Robot-assisted surgery is safe during the learning process, and "learning by doing" improves surgical efficiency. Future studies need to define competencies with broader implications.
Background: Robot-assisted surgery demands a specific skillset of surgical knowledge, skills, and attitudes from the robotic surgeon to function as part of the robotic team and for maximal utility of the assistive surgical robot. Subsequently, the learning process of robot-assisted surgery entails new modes of learning. We sought to systematically summarize the published data on pediatric robot-assisted pyeloplasty (pRALP) to decipher the learning process by analyzing learning curves. Methods: This review followed the PRISMA guidelines. PubMed, EMBASE, Web of Science, and Scopus databases were systematically searched for 'learning curve' AND 'pediatric pyeloplasty'. All studies presenting outcomes of learning curves (LC) in the context of pRALP in patients < 18 years of age were included. Studies comparing LC in pRALP versus open and/or laparoscopic pyeloplasty were also included; however, those solely focusing on LC in non-robotic approaches were excluded. The methodological quality was assessed using the Newcastle and Ottawa scale. Results: Competency was non-uniformly defined in all fifteen studies addressing learning curves in pRALP. pRALP was considered safe at all stages. Proficiency in pRALP was reached after 18 cases, while competency was estimated to demand 31 operated cases with operative duration as outcome variable. Conclusions: Pediatric RALP is safe during the learning process and 'learning by doing' improves efficiency. Competencies with broader implications than time must be defined for future studies.

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