Aeromedical evacuations during the COVID-19 pandemic: practical considerations for patient transport
Bibliographic record
Abstract
Aeromedical evacuation systems around the world face new challenges in light of coronavirus disease 2019 (COVID-19). 1 These challenges include unprecedented demand for patient transfers, as well as increased risk of exposure to aircraft crew due to prolonged close contact with contagious patients. Our organization, Service d'vacuations aromdicales du Qubec (EVAQ, Quebec Aeromedical Evacuation Services) is the medical evacuation service for the Province of Quebec. Every year, over 2000 critical care patients are transferred by our service. Since the declaration of the COVID-19 pandemic by the World Health Organization on March 11, 2020, EVAQ has organized transfers for 50 COVID-19 confirmed or suspected patients. We would like to share some practical considerations from our experience with aeromedical transfer of COVID-19 patients. These concepts are relevant not only to aeromedical transfer crews, but also to the referring and receiving emergency medical teams.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".