High-Fidelity Simulation Versus Traditional Didactic Techniques for Teaching Neurological Emergencies to Neurology Residents: A Feasibility Study. (P1.323)
Bibliographic record
Abstract
OBJECTIVE: to check for the feasibility of teaching neurological emergencies on high-fidelity simulation. INTRODUCTION: The exposure to neurocritical care among neurology residents is highly variable. In spite of proven success of Simulation based learning (SBL) in teaching critical care, emergency medicine, and anesthesiology, studies evaluating SBL in neurocritical care are still lacking. METHODS: Eligible PGY-2 neurology residents (N=10) from Columbia university and Weill-Cornell were randomized into SBL and traditional didactic teaching groups. High-fidelity Sim-Man 3G was used to simulate realistic scenarios of acute ischemic stroke, intracranial pressure (ICP) crisis, and status epilepticus. Learning objectives were assessed using crisis resource management (CRM) assessment tools including identification of key actions (0=no, 1=with prompt, 2=never), Ottawa CRM checklist, and knowledge based pre- and post-intervention tests. Mean±SD, median, and Wilcoxon rank-sum tests were calculated. RESULTS: Acute stroke case: mean key action scores (maximum score 28) were 17.8±1.5 & 16.4±2.9, difference in gain on post-test scores were 0.32±0.3 & 0.2±0.3 after simulation and didactics interventions respectively. ICP crisis case: mean key action scores (maximum score 24) were 16.4±4.6 & 17.3±0.6, difference in gain on post-test scores were 0.14±0.19 & 0.13±0.11, for simulation and didactics groups respectively. Status epilepticus case: mean key action scores (maximum score 38) were 31.8±3.4 & 28±3.9, difference in gain on post-test scores were 0.2±0.24 & 0.28±0.4 after simulation and didactics interventions respectively. Median CRM score were 6 & 4 for stroke and ICP crisis cases, 6 & 6 for status epilepticus cases when comparing SBL and didactic groups. There were no statistically significant differences found between groups for either of the pre-specified outcomes. CONCLUSIONS: Simulation based learning offers promise as a tool for objectively assessing some of the ACGME competencies that are more difficult to evaluate via traditional means. Studies with larger N and longer follow-up are needed to assess the efficacy, learner-retention and satisfaction rates among groups. Study supported by AAN education grant 2013.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".