Simulation training for medical emergencies: Evaluation of dentists’ <scp>long‐term</scp> learning skills and confidence
Bibliographic record
Abstract
INTRODUCTION: As the population ages and more patients experience medical emergencies during dental treatments, dentists must competently and confidently manage these situations. We developed a simulation training course for medical emergencies in the dental setting using an inexpensive vital sign simulation app for smartphones/tablets without the need for an expensive simulator. However, the duration for which this effect is maintained is unclear. This study was performed to evaluate the long-term educational effect at 3, 6, and 12 months after taking the course. MATERIALS AND METHODS: Thirty-nine dental residents participated in this course. Scenarios included vasovagal syncope, anaphylaxis, hyperventilation syndrome, and acute coronary syndrome, each of which the participants had to diagnose and treat. The participants were evaluated using a checklist for anaphylaxis diagnosis and treatment skills immediately after and 3, 6, and 12 months after the course. The participants were also surveyed about their confidence in diagnosing and treating these conditions by questionnaire before, immediately after, and 3, 6, and 12 months after the course. RESULTS: The checklist scores for anaphylaxis were significantly lower at 3, 6, and 12 months after the course than immediately after the course. The percentage of participants who provided a correct diagnosis and appropriate treatment for vasovagal syncope, hyperventilation syndrome, and acute coronary syndrome was lower at all reassessments than immediately after the course. CONCLUSION: Because medical emergency management skills and confidence declined within 3 months, it would be useful to introduce a refresher course approximately 3 months after the initial course to maintain skills and confidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".