A Serious Game for Anesthesia-Based Crisis Resource Management Training
Bibliographic record
Abstract
Simulation-based training has been widely adopted in medical education as a tool in the practice and development of skills within a safe, controlled, and monitored environment. However, significant cost and logistical challenges exist within traditional simulation practices. The rising popularity of gaming has seen the wide application of serious games to medical education and training. Serious gaming (and virtual simulation in general) offers a viable alternative to traditional training practices, offering students/trainees the opportunity to train until they reach a specific competency level in a safe, interactive, engaging, and cost-effective manner for effective skills transfer to the real world. Here we present a serious game for anesthesia-based crisis resource management (ACRM) training. The ACRM serious game provides trainees the opportunity to react to a simulated medical emergency within a virtual operating room while providing an interactive, and engaging training experience. Results of an experiment that was conducted to examine the usability (the ease of use of the serious game and its interface) of the serious game, and its ability to engage trainees, indicate that although improvements to the user interface can be made, it shows promise as an immersive and engaging complementary training tool.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".