Gut It Out: An Anatomy Card Game and Study Tool for Medical Students
Bibliographic record
Abstract
Gamification in medical education involves integrating game principles—including mechanics, design, or experiences—into a learning context. Game‐based approaches in higher education have been shown to improve student engagement, motivation, and performance when applied effectively. Many medical students report feeling overwhelmed by the complexity and content‐heavy nature of anatomy, making it an ideal subject for gamification. In this project, we developed and designed an educational anatomy card game intended for medical students. Students in the inaugural cohort of the Certificate in Biomedical Visualization and Communication (BMVC) at the University of British Columbia teamed up with the Hackspace for Immersive Virtual Experiences (HIVE) and faculty advisors for their capstone project. The team collected survey responses from current medical students to determine desirable game features and dynamics, followed by several rounds of prototyping and piloting. The students also enlisted the help of a subject matter expert in game design. The final card game, entitled “Gut it Out”, is appropriate for players possessing any level of anatomy knowledge, with optional mechanics designed for medical students. Gameplay involves players competing to build fully innervated and vascularized organs. Organ cards specify the number of blood vessel cards (arteries and veins) and nerve cards (somatic, sympathetic, and parasympathetic) required for completion. Additional clinical cards can be played to gain advantages or sabotage opponents. An optional gameplay component challenges players to name each blood vessel and nerve associated with the organs they have completed in exchange for double points. Aesthetics feature a high‐contrast colour palette and bold, simple organ illustrations. The team developed multiple versions of the rules that are currently being play tested by medical students and anatomists. Future directions include incorporating an interactive augmented reality component to enrich the educational value of the organ cards and conducting a mixed methods study to assess the efficacy of the game as a study tool. “Gut it Out” applies gamification to anatomy education to create a fun and educational card game featuring accessible, fast‐paced gameplay. This game was designed to complement and expand the current ecosystem of anatomy resources hosted by the HIVE.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".