Vehicle Dynamics Endured by Patients during Emergency Evacuation—Ambulance versus Helicopter
Bibliographic record
Abstract
In the event of a road accident, a quick intervention is crucial. The mobile emergency services take care of patients whose condition requires an emergency repatriation to a hospital, by land in an ambulance or by air in a helicopter. The main criteria for choosing the means of transport are the time required for repatriation and the patient’s more or less critical state of health. Do the vehicle dynamic effects endured by the transported patient have an influence on their health condition? Vehicle dynamics data were recorded with a road data recorder for a period of 3 months, under real conditions of patient repatriation to a hospital; 39 trips were recorded by ambulance and 29 trips by helicopter. Significant differences in speed (average 42 versus 202 km/h) and distance travelled (average 23 versus 85 km) were observed. The sustained effects are similar in helicopters and ambulances. The ambulance causes more abrupt variations in longitudinal and transversal directions, whereas the helicopter has more variations in vertical direction. The vibration level in helicopters is higher than in ambulances. These results can be considered as a first reference baseline for establishing a characterization of transported patients’ exposure to vehicle dynamics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".