Use of Geographic Information Systems to Determine New Helipad Locations and Improve Timely Response While Mitigating Risk of Helicopter Emergency Medical Services Operations
Bibliographic record
Abstract
INTRODUCTION: Traumatic injury is a leading cause of morbidity and mortality, but these can be minimized by timely transport to definite care. Helicopter emergency medical services (HEMS) provide timely transport and can influence survival. However, accident analyses indicate that landing at an unsecured landing zone (LZ), particularly at night, increases the risk of aviation accidents. To ensure safety, some HEMS operations land only at designated, secured LZs. OBJECTIVE: This study utilized geographic information systems (GISs) to compare locations of scene call requests and secure LZs. The goal was to determine the optimal placement of new helipads as a strategy to improve access while mitigating the risk of aviation accidents. METHODS: Call request data from a large air medical transport service were used to determine the geographic locations of all requests for scene responses in 2006. Request locations were compared with the locations of existing helipads, and straight-line distances between scene and helipad were determined using the GIS application. The application was then used to determine potential locations for new helipads. RESULTS: During the study period, 748 requests for scene calls and 269 helipads were available. There were 476 (52.4%) requests at least 10 kilometers from a helipad and 356 (36.6%) requests at least 15 kilometers from a helipad. One particular region, Southwestern Ontario, was identified as having the highest number of requests >15 kilometers from the closest helipad. CONCLUSION: GISs can be used to determine potential locations for new helipad construction using historical call request data. This evidence-based approach can improve HEMS access while mitigating operational risk.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".