A novel low-cost high-fidelity porcine model of liver metastases for simulation training in robotic parenchyma-preserving liver resection.

In the era of minimally invasive surgery (MIS), parenchyma-preserving liver resections are gaining prominence with the potential to offer improved perioperative outcomes without compromising oncological safety. The surgeon learning curve remains challenging, and simulation plays a key role in surgical training. Existing simulation models can be limited by suboptimal fidelity and high cost.

Development and validation of a simulation-based assessment of operative competence for higher specialist trainees in general surgery.

Background: Simulation is increasingly being explored as an assessment modality. This study sought to develop and collate validity evidence for a novel simulation-based assessment of operative competence. We describe the approach to assessment design, development, pilot testing, and validity investigation.

Design and utilisation of a novel, high-fidelity, low-cost, hybrid-tissue simulation model to facilitate training in robot-assisted partial nephrectomy.

Robot-assisted partial nephrectomy (RAPN) has rapidly evolved as the standard of care for appropriately selected renal tumours, offering key patient benefits over radical nephrectomy or open surgical approaches. Accordingly, RAPN is a key competency that urology trainees wishing to treat kidney cancer must master. Training in robotic surgery is subject to numerous challenges, and simulation has been established as valuable step in the robotic learning curve.

Developing a clamshell thoracotomy training model to support hybrid teaching in simulation-based education.

Objective: Thoracotomy is an acute, time-sensitive procedure. Simulation-based education provides a safe-learning platform to learn these techniques under close supervision.

Methods: We used the spiral model and concepts of functional fidelity to guide the evolutionary design and fabrication of a hybrid thoracotomy simulator.