Simulation-based education to improve management of refractory anaphylaxis in an allergy clinic
Bibliographic record
Abstract
BACKGROUND: High-fidelity simulations based on real-life clinical scenarios have frequently been used to improve patient care, knowledge and teamwork in the acute care setting. Still, they are seldom included in the allergy-immunology curriculum or continuous medical education. Our main goal was to assess if critical care simulations in allergy improved performance in the clinical setting. METHODS: Advanced anaphylaxis scenarios were designed by a panel of emergency, intensive care unit, anesthesiology and allergy-immunology specialists and then adapted for the adult allergy clinic setting. This simulation activity included a first part in the high-fidelity simulation-training laboratory and a second at the adult allergy clinic involving actors and a high-fidelity mannequin. Participants filled out a questionnaire, and qualitative interviews were performed with staff after they had managed cases of refractory anaphylaxis. RESULTS: Four nurses, seven allergy-immunology fellows and six allergy/immunologists underwent the simulation. Questionnaires showed a perceived improvement in aspects of crisis and anaphylaxis management. The in-situ simulation revealed gaps in the process, which were subsequently resolved. Qualitative interviews with participants revealed a more rapid and orderly response and improved confidence in their abilities and that of their colleagues to manage anaphylaxis. CONCLUSION: High-fidelity simulations can improve the management of anaphylaxis in the allergy clinic and team confidence. This activity was instrumental in reducing staff reluctance to perform high-risk challenges in the ambulatory setting, thus lifting a critical barrier for implementing oral immunotherapy at our adult center.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".