P.051 Stroke Care and Neurological Emergency Response Simulation (SCaNERS): High-fidelity acute stroke simulation increases learner confidence in providing acute stroke care
Bibliographic record
Abstract
Background: Resident physicians often observe stroke alerts before managing them alone, which exposes patients to potential harm from trainees’ lack of experience. Simulation training offers a low-risk environment for skill acquisition. This project assessed learners’ confidence in leading stroke codes before and after completing a stroke simulation training program during neurology rotations at the University of Saskatchewan. Methods: High-fidelity simulation cases were developed encompassing several diagnostic and therapeutic goals of acute stroke care. Standardized patients were trained for increased fidelity. Standardized debriefing was given after each session. Pre- and post-simulation surveys captured learner confidence and cognitive load. Results: Pilot data reveal learners’ confidence and comfort in providing acute stroke care, including thrombolysis treatment decisions, significantly increases after simulation training (n=8; p=0.0006-0.01). They also felt more prepared to conduct future acute stroke care (p=0.009). Skills not directly addressed in simulation did not show significant improvement (p=0.09-1.89). Learners consistently rated the session as requiring high mental effort. Conclusions: Implementation of high-fidelity simulation training leads to significant improvement in learner confidence. Future cases will capture additional objectives and ensure acceptable cognitive load. Ongoing data collection to explore residents’ experiences and knowledge improvement in stroke care and assess local reductions in treatment delays is underway.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.005 |
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".