Virtual trauma patient simulation design using the McGill Simulation Complexity Score (MSCS): a breakthrough in trauma education
Bibliographic record
Abstract
BACKGROUND: Virtual patient simulations are interactive, computer-based cases. We designed scenarios based on the McGill Simulation Complexity Score (MSCS), a previously described objective complexity score. We aimed to establish validity of the MSCS and introduce a novel learning tool in trauma education at our institution. METHODS: After design of an easy and difficult patient scenario, we randomized medical students and residents to each perform 1 of the 2 scenarios. We conducted a 2-way analysis of variance of training level (medical student, resident) and scenario complexity (easy, difficult) to assess their effects on virtual time, the number of steps taken in the scenario, beneficial and harmful actions, and the ratio of beneficial over harmful actions. RESULTS: Virtual patient scenarios were successfully designed using the MSCS. Twenty-four medical students and 12 residents participated in the easy scenario (MSCS = 3), and 27 medical students and 12 residents did the difficult scenario (MSCS = 18). Though beneficial actions were similar between students and residents, sudents performed more harmful actions, particularly when the scenario was difficult. One virtual patient died in the easy scenario and 3 died in the difficult one (all medical students). Performance varied with level of complexity and there was significant interaction between level of training and number of steps, as well as with number of harmful actions. Decreasing performance with increasing level of complexity, as defined by the MSCS, suggests this score can accurately quantify difficulty. CONCLUSION: We established validity of the MSCS and showed its successful application on virtual patient scenario design.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".