The McGill Simulation Complexity Score (MSCS): a novel complexity scoring system for simulations in trauma
Bibliographic record
Abstract
BACKGROUND: In medical education, simulation can be defined as an activity in which an individual demonstrates skills, procedures and critical thinking using interactive mannequins in a setting closely resembling the clinical environment. To our knowledge, the complexity of trauma simulations has not previously been assessed. We aimed to develop an objective trauma simulation complexity score and assess its interrater reliability. METHODS: The McGill Simulation Complexity Score (MSCS) was designed to address the need for objective evaluation of the complexity of trauma scenarios. Components of the score reflected the Advanced Trauma Life Support approach to trauma. The score was developed to take into account the severity of trauma injuries and the complexity of their management. We assessed interrater reliability at 5 high-fidelity simulation events. Interrater reliability was calculated using the Pearson correlation coefficient (PCC) and the intraclass correlation coefficient (ICC). RESULTS: The MSCS has 5 categories: airway, breathing, circulation, disability, and extremities or exposure. The scale has 5 levels for each category, from 0 to 4; level increases with complexity, with 0 corresponding to normal or absent. Cases designed to lead to cardiac arrest, regardless of whether or not the trainee has the ability to resuscitate the simulated patient and regardless of the level of each category, are automatically assigned the maximum score. Between 3 and 9 raters used the MSCS to grade the level of complexity of 26 scenarios at the 5 events. The mean MSCS was 10.2 (range 3.0-20.0). Mean PCC and ICC values were both above 0.7 and therefore statistically significant. CONCLUSION: The MSCS for trauma is an innovative scoring system with high interrater reliability.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".