Abstract
Traditionally, surgical skills are meant to be acquired through years of experience in the operating room (OR) on live patients. This chapter introduces the use of cadaveric models in spine surgery, how such laboratories are organized worldwide. Spine surgery is associated with increased morbidity due to the complexity of these surgical procedures, so the increasing need for improvement of postoperative results forces the physicians on enchasing the effectiveness of their surgical standards. Spine surgery’s high exacting nature is common ground due to the complex anatomy as well as the dangers surgeons can face intra- and postoperatively. Cadaveric training programs offer the infrastructure and the personnel to face this need through a combination of theoretical and practical training through up-to-date teaching, make a truly very meaningful advance to improve patient care, and improve the quality of spine surgery. Included in this chapter are the advantages and disadvantages of cadaveric spine surgery, as well as the methods through which spine surgeons are introduced to recognize the anatomical landmarks of each part of the spine and learn how to perform certain surgical procedures of the cervical, thoracic, and lumbar spine. Additionally, the new role of minimally invasive surgery is being presented and how training using cadavers can potentially expand our therapeutic options. Despite some limitations in this type of training, cadaver-based surgical skill learning courses are worldwide recognized as the gold standard in surgical training. New doctors, as well as medical professionals, are encouraged to practice spine surgery on cadaveric models, which gives them the best way to effectively improve their skills and make significant progress in achieving their treatment goals effectively.
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Stamatopoulos, T., Yanamadala, V., Shin, J.H. (2018). Use of Cadaveric Models in Simulation Training in Spinal Procedures. In: Alaraj, A. (eds) Comprehensive Healthcare Simulation: Neurosurgery. Comprehensive Healthcare Simulation. Springer, Cham. https://doi.org/10.1007/978-3-319-75583-0_9
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