A Novel Competency-Based Simulation Model for Thoracoscopic Lung Resection

1Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background Simulation-based thoracic surgery training is increasingly incorporating physical models to enhance traditional learning methods. Conventional box trainers, though useful for basic skills, often lack anatomical accuracy and tactile feedback, limiting their relevance for complex procedures like thoracoscopic lung resection. High-fidelity 3D-printed lung models offer realistic anatomy and procedural flow, but their educational impact remains underexplored. Methods Fifty-two surgical residents without prior thoracoscopic experience were randomly assigned to a high-fidelity lung model group or a conventional Fundamentals of Laparoscopic Surgery (FLS) box trainer group. All participants completed a baseline thoracic anatomy test and received standardized educational materials. The lung model group received structured simulation training on procedural anatomy and operative steps, while the FLS group practiced fundamental laparoscopic tasks. After training, participants repeated the anatomy test and performed a thoracoscopic lung wedge resection in a live animal model. Performance was assessed using the Objective Structured Assessment of Technical Skill (OSATS) and a 5-point confidence scale. Results A total of 52 surgical residents participated in the study, with 26 assigned to the high-fidelity lung model group and 26 to the FLS trainer group. Baseline anatomy scores were similar between groups (65.42± 6.10 vs. 66.12± 5.92; p = 0.710). Posttraining, the lung model group showed greater gains in anatomy comprehension (87.60± 4.75 vs. 78.19± 5.54; p < 0.001), higher OSATS scores (19.18± 2.43 vs. 15.41± 2.41; p < 0.001), and increased confidence (3.13± 0.61 vs. 2.27± 0.68; p = 0.002). Conclusion High-fidelity 3D-printed lung models significantly enhance anatomical understanding, thoracoscopic skills, and confidence compared with conventional box trainers. These results support integrating anatomically accurate simulation into thoracic surgical education to improve both cognitive and psychomotor outcomes.

Cite

CITATION STYLE

APA

Liu, G., Yang, F., Zhou, Z., & Jiang, G. (2026). A Novel Competency-Based Simulation Model for Thoracoscopic Lung Resection. Thoracic and Cardiovascular Surgeon, 74(2), 174–180. https://doi.org/10.1055/a-2702-2239

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free