Flight Diversions Due to Onboard Medical Emergencies on an International Commercial Airline
Bibliographic record
Abstract
INTRODUCTION: Each year, close to 2 billion passengers travel on commercial airlines. In-flight medical events result in suboptimal care due to a variety of factors. Flight diversions due to medical emergencies carry a significant financial and legal cost. The purpose of this study was to determine the causes of in-flight medical diversions from Air Canada. METHODS: This was a review of in-flight medical emergencies from 2004-2008. Both telemedicine and Air Canada databases were crossreferenced to capture all incidents. Presenting complaints were categorized by systems. Descriptive statistics were used to analyze the data. RESULTS: Over the 5 yr, there were 220 diversions, of which 91 (41.4%) of the decisions were made by pilots or onboard medical personnel. During this period there were 5386 telemedicine contacts with ground support providers, who on average recommended 2.4 diversions per 100 calls. The rate for diversions almost doubled from 2006 to 2007, with a sharp drop in telemedicine contacts during the same period. The four most common categories resulting in diversions were cardiac (58 diversions, 26.4%), neurological (43 diversions, 19.5%), gastrointestinal (GI) (25 diversions, 11.4%), and syncope (22 diversions, 10.0%). Only 6.8% of all diversions were due to cardiac arrest. DISCUSSION: Medical conditions most commonly leading to diversions were cardiac, neurological, gastrointestinal, and syncope. Our study showed that a decrease in telemedicine contact during this period was accompanied by an increase in diversions, while increased pre-screening of passengers did not prove effective in decreasing diversion rates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".