Management of In-flight Medical Emergencies
Bibliographic record
Abstract
MORE than two billion passengers board commercial airplanes every year.§ Airline travel is inherently safe and reasonably comfortable, but factors including psychological stress, jet lag, and preexisting disease cause a small number of passengers to become ill while flying.The average age of airline passengers is increasing with that of the general population, and older passengers may have one or more underlying illnesses that impair their ability to adapt to a potentially stressful environment.New, larger airplanes such as the Airbus A380 (Airbus S.A.S., Toulouse, France) may carry twice the number of passengers as current aircraft (more than 800 passengers), which further increases the probability that a medical emergency will occur on any given flight.Physicians who care for a patient who has a serious illness and who wishes to travel may refer to a comprehensive set of medical guidelines for air travel, including a list of contraindications to flight prepared by the Aerospace Medical Association. 1 The regulations governing aviation are complex, and the laws of the aircraft's country of registration primarily regulate training and the provision of emergency medical care aboard airplanes.This article focuses on US Federal Aviation Administration (FAA) regulations that apply to medical emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".