Scholarlink

Background <p>As robotic surgery becomes increasingly integral to general surgical practice, residency programs are rapidly incorporating simulation-based training. The daVinci® SimNow platform offers nearly 50 modules aimed at developing robotic technical skills, yet most lack rigorous construct validation and program directors are left wondering which to require for their trainees. This study evaluates the ability of selected SimNow modules to differentiate resident performance by training level, thereby assessing their educational utility within a structured robotic surgery curriculum.</p> Methods <p>A robotic surgery curriculum incorporating 14 SimNow modules was implemented at a single academic general surgery program. All residents (n = 39) were invited to participate during the 2022–2023 academic year, with 35 completing at least part of the curriculum. For each module, data were collected on number of attempts to achieve passing (score ≥ 90 on two consecutive tries), average score, and time per attempt. Residents were grouped into junior (PGY 1–2) and senior (PGY 3–6) cohorts. Statistical comparisons were made using the Wilcoxon Rank Sum test, and effect sizes were calculated using Cohen’s d.</p> Results <p>Of 2425 total attempts, 490 modules were completed. Senior residents outperformed juniors in at least one performance category on four of the 14 modules (28.5%, <i>p</i> &lt; 0.05). However, 12 modules (85.7%) demonstrated at least a medium effect size (d ≥ 0.5), suggesting meaningful performance differences by training level.</p> Conclusions <p>While only a minority of modules showed statistically significant differences, most demonstrated meaningful effect sizes, supporting partial construct validity. These findings underscore the need for more rigorous validation of simulator-based curricula to ensure educational effectiveness in robotic surgery training.</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

Evaluating the educational utility of SimNow modules in a robotic surgery curriculum for general surgery residents

  • 期刊论文

  • 发表日期：

    2026年3月13日

查看全文
  • Jacob A. Applegarth,
  • Ryan T. Davis,
  • Ibrahim B. Baida,
  • Spencer Wilhelm,
  • Ngan Nguyen,
  • Nathan M. Novotny

摘要

Background

As robotic surgery becomes increasingly integral to general surgical practice, residency programs are rapidly incorporating simulation-based training. The daVinci® SimNow platform offers nearly 50 modules aimed at developing robotic technical skills, yet most lack rigorous construct validation and program directors are left wondering which to require for their trainees. This study evaluates the ability of selected SimNow modules to differentiate resident performance by training level, thereby assessing their educational utility within a structured robotic surgery curriculum.

Methods

A robotic surgery curriculum incorporating 14 SimNow modules was implemented at a single academic general surgery program. All residents (n = 39) were invited to participate during the 2022–2023 academic year, with 35 completing at least part of the curriculum. For each module, data were collected on number of attempts to achieve passing (score ≥ 90 on two consecutive tries), average score, and time per attempt. Residents were grouped into junior (PGY 1–2) and senior (PGY 3–6) cohorts. Statistical comparisons were made using the Wilcoxon Rank Sum test, and effect sizes were calculated using Cohen’s d.

Results

Of 2425 total attempts, 490 modules were completed. Senior residents outperformed juniors in at least one performance category on four of the 14 modules (28.5%, p < 0.05). However, 12 modules (85.7%) demonstrated at least a medium effect size (d ≥ 0.5), suggesting meaningful performance differences by training level.

Conclusions

While only a minority of modules showed statistically significant differences, most demonstrated meaningful effect sizes, supporting partial construct validity. These findings underscore the need for more rigorous validation of simulator-based curricula to ensure educational effectiveness in robotic surgery training.