Virtual Reality Training Improves Procedural Skills in Mannequin-Based Simulation in Medical Students: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Objectives The goal of this study was to evaluate whether immersive virtual reality (VR) training used in conjunction with interactive online learning improved procedural skills in medical students, using chest tube insertion as a model. Methods Medical students (n = 30) with limited or no experience with chest tube insertion were randomized into control and VR groups. All participants received access to a previously developed online module to learn the equipment and steps involved in performing chest tube insertion. The VR group received additional training using commercially available software. All participants were then asked to perform chest tube insertion on a standardized mannequin. Technical skills were assessed by surgical experts, blinded to the group allocation, using a modified Objective Structured Assessment of Technical Skill (OSATS) rating scale (11-items, each scored 1-5). Multiple-choice tests and a 5-point Likert-scale were used to assess theoretical knowledge and to rate confidence level before and after training. Data are presented as median and interquartile range. Results After training, all participants showed significant improvement in knowledge from baseline; rate of correct answers was 50% pre-training [40.0-66.7]; 80% post-training [73.0-93.3]; P < 0.0001). There was no statistically significant difference between the two groups in knowledge before and after training. The VR group spent <60 min in VR training and had better procedural performance (OSATS scores: controls: 39 [33-45]; VR: 46 [42.0-50]; P = 0.03) and higher confidence (controls: 3 [3-4]; VR: 4 [4-5]; P = 0.002). Conclusions Adding VR simulation to online learning improved technical skills and confidence in medical students learning chest tube insertion.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".