Impact of current video game playing on robotic simulation skills among medical students
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of current and prior video game playing on initial robotic simulation skill acquisition. MATERIAL AND METHODS: This cross-sectional descriptive study (Canadian Task Force Classification II-1) was conducted at a medical university training center. The study subjects were medical students who currently played video games (Group I) and those who had not played video games in the last 2 years (Group II). The robotic skills of both groups were assessed using simulation. RESULTS: Twenty-two students enrolled in this study; however, only 21 completed it. The median age of the participants was 23 (22-24) years and 24 (23-26) years in Groups I and II, respectively. Among the participants, 15 (71.4%) were male and 6 (28.5%) were female, and 90.4% of the students started playing video games in primary school. When the 2 groups were compared according to the completion time of each exercise, Group I finished more quickly than Group II in the Peg Board-1 exercise (p>0.05), whereas Group II had better results in 3 exercises including Pick and Place, Ring and Rail, and Thread the Rings-1. However, none of the differences were found to be statistically significant (p>.05), and according to the overall scores based on the time to complete exercises, economy of motion, instrument collision, use of excessive instrument force, instruments out of view, and master workspace range, the scores were not statistically different between Groups I and II (p>.05). CONCLUSION: According to the basic robotic simulation exercise results, there was no difference between medical students who used to play video games and those who still played video games. Studies evaluating baseline visuospatial skills with larger sample sizes are needed.
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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 | 0.013 |
| 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".