Validation of laparoscopy and flexible ureteroscopy tasks in inanimate simulation training models at a large-scale conference setting

被引:2
|
作者
Lu, Jirong [1 ]
Thandapani, Karthik [1 ]
Kuo, Tricia [2 ,3 ]
Tiong, Ho Yee [1 ]
机构
[1] Natl Univ Singapore Hosp, Dept Urol, Singapore, Singapore
[2] Singapore Gen Hosp, Dept Urol, Singapore, Singapore
[3] Sengkang Hlth, Urol Serv, Singapore, Singapore
关键词
Education; Laparoscopy; Simulation; Ureteroscopy; Endourology; SURGICAL SKILLS; BENCH MODEL; SURGERY; ASSOCIATION; FIDELITY;
D O I
10.1016/j.ajur.2019.12.001
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Objective: Simulation based training with training models is being increasingly used as a tool to help trainees mount the learning curve. However, validation studies of surgical sim-ulators are often limited by small numbers. We aim to evaluate the feasibility of validating simulation-training tasks in laparoscopy and flexible ureteroscopy (FURS) rapidly at a large-scale conference setting for residents. Methods: Seventy-six urology residents from various Asian countries were assessed on their laparoscopic and FURS skills during the 14th Urological Association of Asia Congress 2016. Res-idents performed the peg transfer task from the fundamentals of laparoscopic surgery (FLS) and completed inspection of calyces and stone retrieval using a flexible ureteroscope in an en-dourological model. Each participant's experience (no experience, 1-30 or >30 procedures) in laparoscopy, rigid ureteroscopy (RURS) and FURS was self-reported. Results: Median time taken to complete the laparoscopic task decreased with increasing laparoscopic experience (209 s vs. 177 s vs. 145 s, p=0.008) whereas median time taken to complete the FURS tasks reduced with increasing FURS experience (405 s vs. 250 s vs. 163 s, p= 0.003) but not with RURS experience (400.5 s vs. 397 s vs. 331 s, p=0.1 43), demonstrating construct validity. Positive educational impact of both tasks was high, with mean ratings of 4.16/5 and 4.10/5 respectively, demonstrating face validity. Conclusion: Our study demonstrates construct and face validities of laparoscopy and FURS simulation tasks among residents at a conference setting. Validation studies at a conference setting can be an effective avenue for evaluating simulation models and curriculum in the future. (C) 2021 Editorial Office of Asian Journal of Urology. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:215 / 219
页数:5
相关论文
共 50 条
  • [31] Training large-scale language models with limited GPU memory: a survey
    Yu TANG
    Linbo QIAO
    Lujia YIN
    Peng LIANG
    Ao SHEN
    Zhilin YANG
    Lizhi ZHANG
    Dongsheng LI
    Frontiers of Information Technology & Electronic Engineering, 2025, 26 (03) : 309 - 331
  • [32] Training large-scale language models with limited GPU memory: a survey
    Tang, Yu
    Qiao, Linbo
    Yin, Lujia
    Liang, Peng
    Shen, Ao
    Yang, Zhilin
    Zhang, Lizhi
    Li, Dongsheng
    FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2025, : 309 - 331
  • [33] PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
    He, Chaoyang
    Li, Shen
    Soltanolkotabi, Mahdi
    Avestimehr, Salman
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 2021, 139
  • [34] Training Large-Scale News Recommenders with Pretrained Language Models in the Loop
    Xiao, Shitao
    Liu, Zheng
    Shao, Yingxia
    Di, Tao
    Middha, Bhuvan
    Wu, Fangzhao
    Xie, Xing
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 4215 - 4225
  • [35] Visual Diagnostics of Parallel Performance in Training Large-Scale DNN Models
    Wei, Yating
    Wang, Zhiyong
    Wang, Zhongwei
    Dai, Yong
    Ou, Gongchang
    Gao, Han
    Yang, Haitao
    Wang, Yue
    Cao, Caleb Chen
    Weng, Luoxuan
    Lu, Jiaying
    Zhu, Rongchen
    Chen, Wei
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2024, 30 (07) : 3915 - 3929
  • [36] MixPipe: Efficient Bidirectional Pipeline Parallelism for Training Large-Scale Models
    Zhang, Weigang
    Zhou, Biyu
    Tang, Xuehai
    Wang, Zhaoxing
    Hu, Songlin
    2023 60TH ACM/IEEE DESIGN AUTOMATION CONFERENCE, DAC, 2023,
  • [37] Large-scale rigid-flexible-coupling vehicle dynamical system simulation
    Piao, Ming-Wei
    Ding, Yan-Chuang
    Li, Fan
    Zhao, Wen-Zhong
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2008, 14 (05): : 875 - 881
  • [38] Accelerating strategies to the numerical simulation of large-scale models for sequential excavation
    Noronha, M.
    Duenser, Ch.
    Beer, G.
    INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, 2007, 31 (09) : 1071 - 1084
  • [39] EXPERIMENTAL-DESIGN ISSUES FOR LARGE-SCALE SIMULATION-MODELS
    BARTON, RR
    1989 WINTER SIMULATION CONFERENCE PROCEEDINGS, 1989, : 411 - 418
  • [40] Large-scale network simulation techniques: Examples of TCP and OSPF models
    Yaun, GR
    Bauer, D
    Bhutada, HL
    Carothers, CD
    Yuksel, M
    Kalyanaraman, S
    ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, 2003, 33 (03) : 27 - 41