Bibliographic record
Abstract
Purpose The purpose of this paper is to determine the effect of video game and surgical experience on the ability to adapt to and use the neuroArm virtual reality (VR) simulator. Design/methodology/approach A total of 48 participants, comprising video gamers, medical students, surgical residents, and qualified surgeons, were recruited. Subjects played three video games and completed a questionnaire. Three pre‐determined tasks simulating surgical procedures were performed using the simulator. Performance was measured by time for task completion, number of errors, and quality of outcome. Findings Gamers outperformed other groups on all measures of performance at almost every task on the VR simulator. All groups showed interval improvement in performance. As age of participants increased, irrespective of their sex and group, their quality of performance decreased and time to complete tasks increased. Initially, the men outperformed the women at every task, however, the difference decreased with repetition. Research limitations/implications More participants are needed to increase statistical significance of the results, in particular female participants. Practical implications This study showed that gamers adapted rapidly to the neuroArm trainer, which could be attributed to enhanced visual attention and spatial distribution skills from video game play. Therefore, visuospatial skills may become strong elements in the selection criterion for future generations of surgical trainees. Originality/value This study evaluated performance on the neuroArm trainer for the first time. The results provide insight into the design of a training program that helps select and prepare future surgeons for robotic surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".