Selection of helicopter bases and transport modes to minimize pre-hospital times in Iceland
Bibliographic record
Abstract
BACKGROUND: Models for the optimal location of ambulance bases typically focus on response time, but for some patients (e.g., trauma), the total pre-hospital time may matter more. Iceland's geography and population distribution imply that fixed-wing air ambulances are an important mode for transporting emergency patients to one of the small number of hospitals with capabilities to treat patients with time-sensitive conditions. Helicopter ambulances have the potential to improve service for incidents that occur far from the closest airport. METHODS: We formulate models to find optimal helicopter base locations with a primary objective of maximizing incident coverage and secondary objective of minimizing patient transport costs. Our models consider three transport modes (ground, fixed-wing, and helicopter), and evaluate the addition of new helicopter bases to a single existing fixed-wing base, single existing helicopter base, and multiple existing ground ambulance bases. RESULTS: Akureyri and Flúðir are potential locations for a second helicopter base. These locations provide similar overall coverage levels, but effectiveness depends on hospital destination assumptions. Flúðir offers greater benefits if all patients are transported to Reykjavík Hospital, whereas Akureyri performs better if patients are transported to the nearest hospital. CONCLUSIONS: The choice of helicopter base locations depends on factors beyond coverage. Our work shows how optimization models can reveal the impact of operational assumptions on optimal base selection and illustrates a methodology that can be adapted for other countries facing similar geographic challenges.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".