Impact of an Electronic App on Resident Responses to Simulated In-Flight Medical Emergencies: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Health care providers are often called to respond to in-flight medical emergencies, but lack familiarity with expected supplies, interventions, and ground medical control support. OBJECTIVE: The objective of this study was to determine whether a mobile phone app (airRx) improves responses to simulated in-flight medical emergencies. METHODS: This was a randomized study of volunteer, nonemergency resident physician participants who managed simulated in-flight medical emergencies with or without the app. Simulations took place in a mock-up cabin in the simulation center. Standardized participants played the patient, family member, and flight attendant roles. Live, nonblinded rating was used with occasional video review for data clarification. Participants participated in two simulated in-flight medical emergencies (shortness of breath and syncope) and were evaluated with checklists and global rating scales (GRS). Checklist item success rates, key critical action times, GRS, and pre-post simulation confidence in managing in-flight medical emergencies were compared. RESULTS: There were 29 participants in each arm (app vs control; N=58) of the study. Mean percentages of completed checklist items for the app versus control groups were mean 56.1 (SD 10.3) versus mean 49.4 (SD 7.4) for shortness of breath (P=.001) and mean 58 (SD 8.1) versus mean 49.8 (SD 7.0) for syncope (P<.001). The GRS improved with the app for the syncope case (mean 3.14, SD 0.89 versus control mean 2.6, SD 0.97; P=.003), but not the shortness of breath case (mean 2.90, SD 0.97 versus control mean 2.81, SD 0.80; P=.43). For timed checklist items, the app group contacted ground support faster for both cases, but the control group was faster to complete vitals and basic exam. Both groups indicated higher confidence in their postsimulation surveys, but the app group demonstrated a greater increase in this measure. CONCLUSIONS: Use of the airRx app prompted some actions, but delayed others. Simulated performance and feedback suggest the app is a useful adjunct for managing in-flight medical emergencies.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".