Scholarlink

Background <p>Robotic virtual reality (VR) simulators provide risk-free, reproducible training environments that may accelerate skill acquisition. This study evaluated the construct validity and learning curve of a VR robotic simulator among gynaecological surgeons with varying experience levels.</p> Methods <p>Fifty gynaecological surgeons were prospectively assigned to three groups: naïve (0 cases), emerging (≤ 61 cases), and experienced (&gt; 61 cases). Participants performed six standardized VR tasks on the SimNow<sup>®</sup> simulator, each repeated 15 times. Performance metrics included time to completion, instrument path length, and total penalty score. Construct validity was analysed using Kruskal–Wallis and post-hoc tests. Learning curves for naïve surgeons were examined with repeated-measures and CuSUM analyses. Factorial ANOVA with Align and Rank Transformation (ART) assessed the effects of experience and repetition.</p> Results <p>The results showed that performance correlated significantly with experience. Experienced surgeons completed tasks faster, with shorter path lengths and fewer errors than naïves (<i>p</i> &lt; 0.001), confirming construct validity. Emerging surgeons frequently approached experienced performance on basic tasks. Naïve participants improved rapidly over the first 3–5 attempts before plateauing, as confirmed by CuSUM analysis. Factorial ANOVA showed significant effects of both practice and experience (<i>p</i> &lt; 0.001), without interaction, indicating consistent gains from repetition across groups.</p> Conclusion <p>The simulator effectively distinguished experience levels and promoted rapid skill acquisition among naïve surgeons, with performance plateauing after 3–5 trials. These findings support integrating VR simulation into gynaecological robotic training to enhance early proficiency. Further research should link simulator outcomes with in vivo surgical performance and refine credentialing standards.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Construct validity and learning curve of six basic skill simulator exercises utilised by the Society of European Robotic Gynaecological Surgery (SERGS)

  • 期刊论文

  • 发表日期：

    2025年11月6日

查看全文
  • Nana Gomes,
  • Thomas Hebert,
  • Marielle Nobbenhuis,
  • Henrik Falconer,
  • Thomas Ind,
  • Ayesha Ahmed,
  • Averyl Bachi,
  • IIse Baeten,
  • Alba Bejrami,
  • Magdalena Bizori,
  • Dhivya Chandrasekaran,
  • Rahul Chatterjee,
  • Tom Coia,
  • Charlotte Dattatreya,
  • Mohammad Eddama,
  • Robyn Edney,
  • Lili Ellison,
  • Ohad Feldstein,
  • Sergi Fernandez,
  • Faiza Gaba,
  • Krish Harikrishnan,
  • Owen Heath,
  • Jonathan Hedges,
  • Tim Hookway,
  • Ollie Jordan,
  • Shaheen Khazali,
  • Hara Koukouli,
  • Letitia Lloyd-Davis,
  • Ifigenia Effie Mantrali,
  • Fecha Manu,
  • Anna McDougall,
  • Alison Montgomery,
  • Tanushree Motiwale,
  • Roopa Nair,
  • Jola Olugbemi,
  • Stefania Palmieri,
  • Lena Petrovic,
  • Olivia Raglan,
  • Irene Ray,
  • Nicola Ryan,
  • Raza Sayyed,
  • Shareen Safinaz,
  • Shiraz Aslam,
  • Izzie Stopford,
  • Rakesh Thing,
  • Arvind Vashisht,
  • Maria Wooley,
  • Ran Xiong

摘要

Background

Robotic virtual reality (VR) simulators provide risk-free, reproducible training environments that may accelerate skill acquisition. This study evaluated the construct validity and learning curve of a VR robotic simulator among gynaecological surgeons with varying experience levels.

Methods

Fifty gynaecological surgeons were prospectively assigned to three groups: naïve (0 cases), emerging (≤ 61 cases), and experienced (> 61 cases). Participants performed six standardized VR tasks on the SimNow® simulator, each repeated 15 times. Performance metrics included time to completion, instrument path length, and total penalty score. Construct validity was analysed using Kruskal–Wallis and post-hoc tests. Learning curves for naïve surgeons were examined with repeated-measures and CuSUM analyses. Factorial ANOVA with Align and Rank Transformation (ART) assessed the effects of experience and repetition.

Results

The results showed that performance correlated significantly with experience. Experienced surgeons completed tasks faster, with shorter path lengths and fewer errors than naïves (p < 0.001), confirming construct validity. Emerging surgeons frequently approached experienced performance on basic tasks. Naïve participants improved rapidly over the first 3–5 attempts before plateauing, as confirmed by CuSUM analysis. Factorial ANOVA showed significant effects of both practice and experience (p < 0.001), without interaction, indicating consistent gains from repetition across groups.

Conclusion

The simulator effectively distinguished experience levels and promoted rapid skill acquisition among naïve surgeons, with performance plateauing after 3–5 trials. These findings support integrating VR simulation into gynaecological robotic training to enhance early proficiency. Further research should link simulator outcomes with in vivo surgical performance and refine credentialing standards.