Training in Neurorhinology: The Impact of Case Volume on the Learning Curve

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Learning curve

Endoscopic endonasal surgery has a long learning curve that is attributable to multiple factors: unfamiliar endoscopic anatomy, lack of endoscopic skills, potential risk of neural and vascular injury, and reconstructive challenges. The learning curve must deal with issues of endoscopic anatomy, instrumentation, 2-dimensional visualization, team dynamics, and dealing with complications. An incremental training plan has been proposed that is applicable to both otolaryngologists and neurosurgeons

Evidence-based review

A PubMed review of the medical literature was performed using the search terms “learning curve, skull base” and “learning curve, cranial base.” Article selection was restricted to the period 1996 to 2010. Guidelines established by the Oxford Center for Evidence-Based Medicine levels of evidence were used to grade the publications.4

Specific data regarding endoscopic endonasal skull base surgery is limited and deals primarily with pituitary surgery (Table 2). Most surgical teams have evaluated

Discussion

The published literature on endoscopic endonasal skull base surgery is limited by small series of patients, nonstandardized measures of the learning curve, and exclusion of relevant factors. Other issues to consider include the specialty of the surgeon, the region of the skull base, and the quality of the learning experience. Otolaryngologists and neurosurgeons differ in regard of prior endoscopic experience, volume of endoscopic cases unrelated to the skull base, necessary surgical skills, and

Recommendations

Current recommendations in the literature for volume of cases to achieve surgical proficiency are based on inadequate data and fail to consider relevant factors that affect the learning curve. The current grade of recommendation based on evidence-based medicine guidelines is “C.” To improve the level of evidence, a precise definition of surgical proficiency with well-defined and validated measures is needed. It is naïve to claim that there is no learning curve or to assume that the learning

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