Using Simulation Technology to Improve Patient Safety in Airway Management by Practicing Otolaryngologists
Bibliographic record
Abstract
OBJECTIVE: Simulation technology provides a safe environment to learn crisis resource management in stressful clinical scenarios, such as the acute airway. While a number of surgical simulation studies have assessed trainees, there remains a paucity of data on simulation benefits for practicing physicians. The objective of this study was to investigate the impact of a simulation symposium on airway management for practicing otolaryngologists. METHODS: Questionnaires (5-point Likert and open-answer questions) and interviews were distributed and conducted at a simulation symposium on airway management held at an annual meeting. RESULTS: The majority of participants had no prior experience in simulation (62.5%). The data suggested a strong increase in comfort with airway management scenarios (2.93 to 4.09 (p<0.001)). Participants reported the symposium as relevant (4.68) and useful (4.67), with increased confidence about their knowledge of crisis resource management and team training (4.53). Qualitative data suggested great educational value for technical skills and communication strategies. CONCLUSION: Simulation with feedback may provide an opportunity for the practicing otolaryngologist to fulfill Continuing Medical Education and Professional Development requirements. This symposium allowed practicing otolaryngologists, including those in the community, to learn, develop, and refresh technical and communication skills while fulfilling certification requirements.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".