Serious Games in Surgical Medical Education: A Virtual Emergency Department as a Tool for Teaching Clinical Reasoning to Medical Students
Bibliographic record
Abstract
BACKGROUND: Serious games enable the simulation of daily working practices and constitute a potential tool for teaching both declarative and procedural knowledge. The availability of educational serious games offering a high-fidelity, three-dimensional environment in combination with profound medical background is limited, and most published studies have assessed student satisfaction rather than learning outcome as a function of game use. OBJECTIVE: This study aimed to test the effect of a serious game simulating an emergency department ("EMERGE") on students' declarative and procedural knowledge, as well as their satisfaction with the serious game. METHODS: This nonrandomized trial was performed at the Department of General, Visceral and Cancer Surgery at University Hospital Cologne, Germany. A total of 140 medical students in the clinical part of their training (5th to 12th semester) self-selected to participate in this experimental study. Declarative knowledge (measured with 20 multiple choice questions) and procedural knowledge (measured with written questions derived from an Objective Structured Clinical Examination station) were assessed before and after working with EMERGE. Students' impression of the effectiveness and applicability of EMERGE were measured on a 6-point Likert scale. RESULTS: A pretest-posttest comparison yielded a significant increase in declarative knowledge. The percentage of correct answers to multiple choice questions increased from before (mean 60.4, SD 16.6) to after (mean 76.0, SD 11.6) playing EMERGE (P<.001). The effect on declarative knowledge was larger in students in lower semesters than in students in higher semesters (P<.001). Additionally, students' overall impression of EMERGE was positive. CONCLUSIONS: Students self-selecting to use a serious game in addition to formal teaching gain declarative and procedural knowledge.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".