Is There a Doctor on Board?: In-Flight Sudden Cardiac Arrest and Automated External Defibrillator Use
Bibliographic record
Abstract
In-flight sudden cardiac arrest (IFCA) is rare yet highly fatal aboard commercial flights, accounting for up to 86% of all in-flight casualties. The most prominent risk factors for IFCA include male sex, age, pre-existing cardiac disease, and duration of flight time. Prompt recognition of IFCA, cardiopulmonary resuscitation, and automated external defibrillator (AED) use are all strongly associated with improved patient outcomes; survival is approximately 6% without an AED present but rises to 21-70% with AED use. Research has confirmed that AEDs are highly sensitive, reliable in turbulent environments, and cost-effective. However, the confined environment of an airline imposes unique challenges in the response to IFCA, such as limited space, delayed access to medical equipment, and delayed diversion times often exceeding the 3-5-minute recommendation for defibrillation. Despite their utility, many countries lack mandated legislation in requiring AEDs on flights, which highlights a fundamental gap in treating IFCA. Crew cardiopulmonary resuscitation proficiency is another cornerstone in IFCA response, yet there is no specific standard for airline-specific CPR training. Live telemedicine in-flight provides physicians and staff the ability to navigate IFCA efficiently and acts as a novel tool that can be used efficaciously. Good Samaritan legal protections reduce liability concerns and encourage intervention yet is not an international standard and has significant regional variability. Recommendations including universal AED placement on commercial flights, standardized airline-specific CPR and AED training, telemedicine integration, and enhanced awareness of legal liability may act to improve passenger survival from IFCA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".