Serious gaming and virtual reality in the multimodal training of laparoscopic inguinal hernia repair: a randomized crossover study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess the transferability of surgical skills for the laparoscopic hernia module between the serious game Touch Surgery™ (TS) and the virtual reality (VR) trainer Lap Mentor™. Furthermore, this study aimed to collect validity evidence and to discuss "sources of validity evidence" for the findings using the laparoscopic inguinal hernia module on TS. METHODS: In a randomized crossover study, medical students (n = 40) in their clinical years performed laparoscopic inguinal hernia modules on TS and the VR trainer. TS group started with "Laparoscopic Inguinal Hernia Module" on TS (phase 1: Preparation, phase 2: Port Placement and Hernia Repair), performed the module first in training, then in test mode until proficiency was reached. VR group started with "Inguinal Hernia Module" on the VR trainer (task 1: Anatomy Identification, task 2: Incision and Dissection) and also performed the module until proficiency. Once proficiency reached in the first modality, the groups performed the other training modality until reaching proficiency. Primary endpoint was the number of attempts needed to achieve proficiency for each group for each task/phase. RESULTS: Students starting with TS needed significantly less attempts to reach proficiency for task 1 on the VR trainer than students who started with the VR trainer (TS = 2.7 ± 0.6 vs. VR = 3.2 ± 0.7; p = 0.028). No significant differences for task 2 were observed between groups (TS = 2.3 ± 1.1 vs. VR = 2.1 ± 0.8; p = 0.524). For both phases on TS, no significant skill transfer from the VR trainer to TS was observed. Aspects of validity evidence for the module on TS were collected. CONCLUSION: The results show that TS brought additional benefit to improve performances on the VR trainer for task 1 but not for task 2. Skill transfer from the VR trainer to TS could not be shown. VR and TS should thus be used in combination with TS first in multimodal training to ensure optimal training conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".