B.4 Stroke care and neurological emergency response simulation (SCaNERS): high-fidelity acute stroke simulation and its impact on knowledge retention
Bibliographic record
Abstract
Background: Stroke simulation-based training has been associated with improved stroke quality metrics. The purpose of this study was to assess whether high-fidelity acute stroke simulation participation led to better knowledge retention one month post simulation in off-service residents. Methods: Off-service residents were provided with non-mandatory pre-simulation pre-reading on stroke. Immediately before stroke simulation, they completed a questionnaire to test their knowledge on a set of 8 questions related to stroke. Immediately post-stroke simulation, they were provided with a debrief including teaching on stroke. After the debrief and one month later, they completed the same questionnaire again. Results: There were a total of 16 off-service resident participants. Wilcoxon signed ranks test was performed. There was a significant difference between pre-simulation and immediate post-simulation scores on the knowledge retention questionnaire (p = 0.008). There was a significant difference between pre-simulation and one-month post-simulation on the knowledge retention questionnaire (p = 0.007). There was no difference between immediate post-simulation and one-month post-simulation on the knowledge retention questionnaire (p = 0.77). Conclusions: Participants performed better on the questionnaire after the simulation, and this improved performance was retained at one month. This is the first study to demonstrate delayed knowledge retention in stroke simulation literature.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".