P.061 Stroke Care and Neurological Emergency Response Simulation (SCaNERS): Creation and Implementation into a Resident Curriculum
Bibliographic record
Abstract
Background: Resident physicians often observe stroke alerts before managing them alone. However, this practice exposes patients to potential harm from trainees’ lack of experience. To address this, we created a acute stroke simulation course. Simulation training offers a low-risk environment for skill acquisition, complimenting the Royal College’s recent transition away from a time-based to competency-based learning curriculum. The purpose of this project was to develop and implement a stroke simulation training program into resident neurology rotations at the University of Saskatchewan. Methods: Six high-fidelity acute stroke simulation cases were developed with the aid of a Simulation Operation Specialist. We identified objectives corresponding to Royal College Entrustable Professional Activities for Adult Neurology encompassing several diagnostic and therapeutic goals of acute stroke care. To increase fidelity, a standardized patient was recruited and trained on how to respond to neurologic exams given a specific stroke syndrome. A standardized debrief was given after each session in a safe, non-judgemental environment. Results: Simulation sessions have been running monthly since March 25, 2021. Conclusions: The creation and implementation of high-fidelity simulation training into a resident curriculum is feasible. Ongoing data is being collected to explore residents’ experiences and knowledge improvement in stroke, and to asses local reductions in treatment delays.
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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 | 0.007 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".