High-Fidelity Simulation-Based Education: Description of an Original Crisis Resource Management and Sedation Learning for Dental Surgeons
Bibliographic record
Abstract
Dental surgery includes invasive procedures performed under sedation or monitored anesthesia care (MAC). It is associated with respiratory risks, resulting in death or neurological sequelae without prompt and appropriate management. Management of airway complications also implies mastering crisis resource management (CRM) principles, essentially non-technical skills to improve patient safety. In response to the need to enhance patient safety and to securely perform surgical procedures outside the operating room due to reduced surgical activity during the worldwide spread of the COVID-19 pandemic, we realized, in our simulation center, a course based on high fidelity simulation to teach procedural sedation and management of related complications. The simulation center accredited this educational program as a continuing professional development formation. The course includes technical skills practice, theoretical presentation, and mastering non-technical skills related to CRM principles. This brief report describes a relatively innovative teaching technique in dentistry, highlights its interest, and reports the subjective opinion of learners as to the pedagogical and professional impact of this training. A learner's satisfaction survey supports the utility of our sedation and CRM programs. A high degree of satisfaction and perceived value reflect robust learners' engagement. All medical specialties should encourage high-fidelity simulation continuing professional development courses that incorporate technical skills and crisis management principles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".