Modelling optimal location for pre-hospital helicopter emergency medical services
Bibliographic record
Abstract
BACKGROUND: Increasing the range and scope of early activation/auto launch helicopter emergency medical services (HEMS) may alleviate unnecessary injury mortality that disproportionately affects rural populations. To date, attempts to develop a quantitative framework for the optimal location of HEMS facilities have been absent. METHODS: Our analysis used five years of critical care data from tertiary health care facilities, spatial data on origin of transport and accurate road travel time catchments for tertiary centres. A location optimization model was developed to identify where the expansion of HEMS would cover the greatest population among those currently underserved. The protocol was developed using geographic information systems (GIS) to measure populations, distances and accessibility to services. RESULTS: Our model determined Royal Inland Hospital (RIH) was the optimal site for an expanded HEMS - based on denominator population, distance to services and historical usage patterns. CONCLUSION: GIS based protocols for location of emergency medical resources can provide supportive evidence for allocation decisions - especially when resources are limited. In this study, we were able to demonstrate conclusively that a logical choice exists for location of additional HEMS. This protocol could be extended to location analysis for other emergency and health services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".