Validation of Two Novel and Complementary Training Platforms for Small Joint Arthroscopy

Area of Science:

• Surgical simulation
• Medical education
• Orthopedic surgery

Background:

• Developing effective training tools is crucial for mastering surgical skills.
• Low-fidelity models offer a cost-effective alternative for repetitive practice.

Purpose of the Study:

• To establish the face and construct validity of two novel low-fidelity models for small joint arthroscopy training.
• To assess the efficacy of these models in differentiating skill levels among surgeons.

Main Methods:

• Two models were created: a 'foundation model' using lemon and radish, and an 'advanced model' using a chicken knee.
• Novice, intermediate, and experienced participants performed simulated arthroscopy tasks.
• Performance was measured by time and accuracy scores, and face validity was assessed via surveys.

Main Results:

• Experienced surgeons consistently outperformed novices and intermediates in both time and accuracy across both models.
• Face validity scores were high (4.2/5 and 4.5/5), indicating perceived realism by experienced surgeons.
• Significant differences in task completion times and scores were observed between experience groups.

Conclusions:

• The developed models possess both construct and face validity for small joint arthroscopy simulation.
• These models are effective, low-cost, and readily available tools for high-repetition surgical training.
• The models successfully differentiate performance based on surgical experience.