Case-Based Virtual Reality Simulation for Severe Pelvic Trauma Clinical Skill Training in Medical Students: Design and Pilot Study
Bibliographic record
Abstract
Background Teaching severe pelvic trauma poses a significant challenge in orthopedic surgery education due to the necessity of both clinical reasoning and procedural operational skills for mastery. Traditional methods of instruction, including theoretical teaching and mannequin practice, face limitations due to the complexity, the unpredictability of treatment scenarios, the scarcity of typical cases, and the abstract nature of traditional teaching, all of which impede students’ knowledge acquisition. Objective This study aims to introduce a novel experimental teaching methodology for severe pelvic trauma, integrating virtual reality (VR) technology as a potent adjunct to existing teaching practices. It evaluates the acceptability, perceived ease of use, and perceived usefulness among users and investigates its impact on knowledge, skills, and confidence in managing severe pelvic trauma before and after engaging with the software. Methods A self-designed questionnaire was distributed to 40 students, and qualitative interviews were conducted with 10 teachers to assess the applicability and acceptability. A 1-group pretest-posttest design was used to evaluate learning outcomes across various domains, including diagnosis and treatment, preliminary diagnosis, disease treatment sequencing, emergency management of hemorrhagic shock, and external fixation of pelvic fractures. Results A total of 40 students underwent training, with 95% (n=38) affirming that the software effectively simulated real-patient scenarios. All participants (n=40, 100%) reported that completing the simulation necessitated making the same decisions as doctors in real life and found the VR simulation interesting and useful. Teacher interviews revealed that 90% (9/10) recognized the VR simulation’s ability to replicate complex clinical cases, resulting in enhanced training effectiveness. Notably, there was a significant improvement in the overall scores for managing hemorrhagic shock (t39=37.6; 95% CI 43.6-48.6; P<.001) and performing external fixation of pelvic fractures (t39=24.1; 95% CI 53.4-63.3; P<.001) from pre- to postsimulation. Conclusions The introduced case-based VR simulation of skill-training methodology positively influences medical students’ clinical reasoning, operative skills, and self-confidence. It offers an efficient strategy for conserving resources while providing quality education for both educators and learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".