Simulation training for medical emergencies of dental patients: A review of the dental literature
Bibliographic record
Abstract
In recent years, due to the aging of the population, the number of dental patients with comorbidities such as hypertension and diabetes has increased. Although it has been reported that these patients are increasingly developing medical emergencies during their dental treatments, many dental providers still do not possess the skills to manage medical emergencies appropriately. Simulation training is essential to improve this situation however, there is no report describing how to conduct an effective simulation in detail for dental office medical emergencies. The purpose of this review is to provide information on simulations that is effective and practical. The authors will highlight the key characteristics for providing effective simulation trainings, such as the selection of simulators, simulation locations, instructors, debriefings, methods for evaluating educational effectiveness, and the use of telesimulation as a method for simulation training due to the global COVID-19 pandemic. In addition, this review provides recommendations on tailoring an ideal simulation training course for those who wish to create one. The authors hope that this review will promote the spread of effective simulation training and in turn, contribute to improving the medical safety of dental patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".