The Big Bang: A Virtual Subarachnoid Hemorrhage Simulation for Preclinical Medical Students
Bibliographic record
Abstract
Simulation-based learning is important for rare, high mortality cases, which are unlikely to be witnessed during clinical rotations but are likely to be encountered during future practice such as a subarachnoid hemorrhage. Neurology case simulations, especially those targeted at preclinical learners, are underrepresented in simulation pedagogy, and preclinical learners are underrepresented in a meta-analysis of the efficacy of simulation-based medical education. We designed a virtual simulation of subarachnoid hemorrhage for preclinical medical students, which can be implemented during restricted access to clinical learning. The simulation is 15 minutes long and requires only one standardized patient and one evaluator, which makes this simulation accessible to institutions with limited simulation resources. We adapted the validated questions from the "Simulation Evaluation Tool - Modified" for our post-simulation survey, which will detect the students' level of confidence and their perceived learning post-simulation. The analysis of student experiences using this validated tool will contribute to the literature base surrounding the efficacy of virtual simulation as a training tool for preclinical learners.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".