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Progressive enhancement in cadaver-based trauma surgery simulation: A 22-course longitudinal evaluation of
Federico Maria Mongardini1, Giovanni Bellanova2, Stefano Calderale3
1Division of General, Mini-invasive, Oncologic and Obesity Surgery, University of Campania Luigi Vanvitelli, Naples, Italy.
Background:
Reduced operative exposure has increased the need for high-fidelity simulation in trauma surgery training. We evaluated whether progressive enhancement of a cadaver-based trauma course from non-perfused to perfused and mechanically ventilated models was associated with improved participant-reported educational outcomes.
Methods:
We performed a longitudinal observational study of 22 consecutive editions (2018-2025) of a standardized course on advanced vascular control in massive thoraco-abdominal hemorrhage. A total of 176 surgeons and surgical trainees attended. Participants rated comparison with other training modalities, learning, realism and satisfaction using a 10-point scale. Outcomes were compared across three cadaveric modalities: non-perfused (NP), perfused (PP) and perfused-reventilated (PR).
Results:
Mean overall ratings increased across modalities (NP: 8.10, PP: 9.23, PR: 9.75; p < 0.001). The largest improvement followed the introduction of vascular perfusion. Addition of mechanical ventilation was associated with further gains, particularly in learning and satisfaction domains. High-fidelity models also generated marked ceiling-effects, limiting score discrimination at the upper end of the scale.
Conclusion:
Progressive increases in cadaver-model fidelity were associated with consistently higher participant-reported realism, learning, and satisfaction. These findings support the educational value of perfused and mechanically ventilated cadaver models for advanced trauma training. Because all outcomes are based on participant perceptions, future studies should incorporate objective performance measures and report resource requirements to further define the practical and educational return of these models.