Enhancing difficult airway management training: the role of virtual reality and adaptive learning
Bibliographic record
Abstract
Abstract Emergency physicians play a central role in healthcare. They often must make quick and accurate decisions to save patients’ lives. Among the critical procedures they have to master is difficult airway management (DAM), a procedure required to establish and maintain a patient’s airway for adequate ventilation and oxygenation. To ensure optimal proficiency in DAM, the clinical skills that comprise this procedure must be regularly practiced and updated. However, traditional training approaches present significant organizational challenges in terms of time and cost. In response to these issues, we have developed an innovative education and training application employing immersive Virtual Reality (VR) for teaching basic to advanced DAM procedures, supported by an Adaptive Learning system. To evaluate the effectiveness of our DAM training system, we conducted experiments with a control group trained using traditional methods and two VR subgroups, one with and one without the Adaptive Learning component. Our results show that simulating the DAM procedure in VR is effective in improving students’ knowledge and produces comparable learning outcomes to traditional teaching methods. Interestingly, our study did not provide conclusive evidence that the adaptive design was superior to the non-adaptive one in terms of knowledge and acquisition of skills. However, it demonstrated greater efficiency, particularly in reducing training time.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".