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Evaluation of the simulation degree of 3D printed removable pituitary tumor resection modelChinese Full Text

GU Xiao;PAN Zhou-xian;CHEN Shi;SHEN Zhen;NIU Lu-lu;PAN Hui;YAO Yong;Eight-year Program of Clinical Medicine,Peking Union Medical College Hospital,CAMS & PUMC;Department of Allergy Treatment,Peking Union Medical College Hospital,CAMS & PUMC;Department of Endocrinology,Peking Union Medical College Hospital,CAMS & PUMC;Institute of Automation,Chinese Academy of Sciences (CASIA);School of Artificial Intelligence,University of Chinese Academy of Sciences;Department of Neurosurgery,Peking Union Medical College Hospital,CAMS & PUMC;

Abstract: Objective To explore the simulation degree of the 3 D printed removable transnasal pituitary adenoma resection model, and to discuss the possibility of teaching operation with the model. Methods Using patient CT modeling, fine optimization and 3 D printed technology, a removable pituitary adenoma resection model was constructed. Nineteen evaluators from Peking Union Medical College Hospital were selected, who were divided into “lower seniority group”(seniority <10 years) and “higher seniority group”(seniority ≥10 years). Each physician operated on the 3 D printed pituitary adenoma resection model. They filled out questionnaires and evaluation scales before and after the operation. Results The higher seniority group have more operation experience than the lower seniority group. The scores of the simulation degree items are all around 4, and there is no significant difference found in different scoring items. The simulation degree score of “pituitary adenoma removal” is relatively low. Thescores from the higher seniority group are generally lower than those of the lower seniority group. Before the operation, the scores in the lower seniority group were significantly lower than those of the higher seniority group, while the confidence scores of the two groups after the operation tended to be the same. The difference between scores before and after operation were significantly higher in lower seniority group(P<0.05). Conclusions The model has high simulation degree and can be used in teaching experiments.
  • DOI:

    10.16352/j.issn.1001-6325.2022.02.020

  • Series:

    (E) Medicine & Public Health

  • Subject:

    Oncology

  • Classification Code:

    R736.4

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