In-Flight Medical Emergencies
Bibliographic record
Abstract
IMPORTANCE: In-flight medical emergencies (IMEs) are common and occur in a complex environment with limited medical resources. Health care personnel are often asked to assist affected passengers and the flight team, and many have limited experience in this environment. OBSERVATIONS: In-flight medical emergencies are estimated to occur in approximately 1 per 604 flights, or 24 to 130 IMEs per 1 million passengers. These events happen in a unique environment, with airplane cabin pressurization equivalent to an altitude of 5000 to 8000 ft during flight, exposing patients to a low partial pressure of oxygen and low humidity. Minimum requirements for emergency medical kit equipment in the United States include an automated external defibrillator; equipment to obtain a basic assessment, hemorrhage control, and initiation of an intravenous line; and medications to treat basic conditions. Other countries have different minimum medical kit standards, and individual airlines have expanded the contents of their medical kit. The most common IMEs involve syncope or near-syncope (32.7%) and gastrointestinal (14.8%), respiratory (10.1%), and cardiovascular (7.0%) symptoms. Diversion of the aircraft from landing at the scheduled destination to a different airport because of a medical emergency occurs in an estimated 4.4% (95% CI, 4.3%-4.6%) of IMEs. Protections for medical volunteers who respond to IMEs in the United States include a Good Samaritan provision of the Aviation Medical Assistance Act and components of the Montreal Convention, although the duty to respond and legal protections vary across countries. Medical volunteers should identify their background and skills, perform an assessment, and report findings to ground-based medical support personnel through the flight crew. Ground-based recommendations ultimately guide interventions on board. CONCLUSIONS AND RELEVANCE: In-flight medical emergencies most commonly involve near-syncope and gastrointestinal, respiratory, and cardiovascular symptoms. Health care professionals can assist during these emergencies as part of a collaborative team involving the flight crew and ground-based physicians.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".