Design and Validity Evidence for a Unique Endoscopy Simulator Using a Commercial Video Game
Bibliographic record
Abstract
Background Procedural simulation enhances early endoscopy training. Multiple commercial simulators are available; however, their application is limited by cost and poor user compliance. First-person "shooter" (FPS) video games are popular. In this study, we aimed to show that a novel in-house designed colonoscope controller used to play an FPS video game shares similar constructs with real-life endoscopy. Methodology Participants completed the first three levels on an FPS video game, Portal (Valve Corporation, Bellevue, WA), first using a conventional controller and then the modified endoscope controller. A total of 12 expert endoscopists and 12 surgical residents with minimal endoscopy experience were evaluated based on completion time, button presses, and hand motion analyses. Results Experts outperformed novices for completion time (expert: 944 seconds; novice: 1,515 seconds; p = 0.006) and hand movements (expert: 1,263.1; novice: 2,052.6; p = 0.004) in using the novel colonoscope controller. There was no difference in button presses or total path length traveled. Furthermore, performance did not differ using conventional game controls. Conclusions Experts outperformed novices using the endoscope but not the conventional controller with respect to the economy of movement and completion time. This result confirms that our endoscope-controlled video game shares similar paradigms with real-life endoscopy and serves as a first step toward creating a more enjoyable and cheaper alternative to commercially available endoscopy simulators.
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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 | 0.041 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".