Managing the airway catastrophe: Longitudinal simulation-based curriculum to teach airway management
Bibliographic record
Abstract
BACKGROUND: A longitudinal curriculum was developed in conjunction with anesthesiologists, otolaryngologists, emergency physicians and experts in medical simulation and education. METHODS: Residents participated in four different simulation-based training modules using animal models, cadavers, task trainers, and crisis scenarios using high fidelity manikins. Scenarios were based on various clinical settings (i.e. emergency room, operating room) and were followed by video-assisted structured debriefings. Participants completed both a self-assessment questionnaire and an exit survey using five-point Likert scales. RESULTS: 31 otolaryngology residents participated in the curriculum. Residents reported simulation training significantly improved technical skills such as tracheostomy, cricothyroidotomy and pediatric intubation (p < 0.05 for all). Non-technical skills, including communication, delegation and management were significantly improved on post-test surveys in simulated crisis scenarios (p < 0.05 for all). 90 (28/31) of participants found simulations to be very realistic. Junior residents placed increased value on didactic teaching and procedural skills, while senior residents on crisis scenarios. Survey results indicated that > 90% (28/31) of participants found the modules of the curriculum to be useful and would recommend them to others. CONCLUSION: A longitudinal simulation-based medical curriculum can be an effective method to teach airway management and teamwork skills to otolaryngology residents.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".