Can gamified surgical sets improve surgical instrument recognition and student performance retention in the operating room? A multi-institutional experimental crossover study
Bibliographic record
Abstract
INTRODUCTION: Surgery requires a high degree of precision, speed, and concentration. Owing to the complexity of the modern world, traditional methods cannot meet these requirements. Therefore, in this study, we investigated students' diagnostic skills in the Operating Room in the context of surgical instruments by using gamification of surgical instruments and a crossover design. METHOD: The study design was a multi-institutional quasi-experimental crossover and involved a three-arm intervention (with gender-specific block randomisation: Group A, B, and C) with a pre-test and three post-tests. A total of 90 students fell into three groups of 30 participants each. The surgical sets were learned for one semester through game-based instruction and traditional teaching, and then three OSCE tests were administered with time and location differences. Using one-way ANOVA, OSCE results were compared in the game, traditional, and control groups. The effectiveness of the intervention was tested in each group by repeated measures. RESULT: The pretest scores of all three groups did not differ significantly. In the OSCE tests, both groups, A and B, performed similarly. However, these tests showed a significant difference in grouping between training through games and training in the traditional way. There was no significant difference between OSCE tests 2 and 3 in the game-based training group, indicating that what was learned was retained, while in the traditional method training group, OSCE 3 test scores declined significantly. Furthermore, repeated measures showed the effectiveness of game-based training. CONCLUSION: In this study, gamification has turned out to be very effective in helping learners learn practical skills and leading to more sustainable learning.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".