Emergency Medical Services Response Times to Motor Vehicle Crashes Increased over the Period 1987 to 2015
Bibliographic record
Abstract
Emergency Medical Services (EMS) response time to motor vehicle crashes (MVC’s) have been studied to determine if reducing the individual components of EMS response time (notification, arrival at the crash scene, and hospital arrival) may affect survival rates. It has been proposed that a reduction to 1 and 15- minute EMS notification and arrival times at the crash would result in 1.84% and 5.2% fewer fatalities. The aim of this study was to analyze the changes in EMS response times (notification, arrival at the crash scene, and hospital arrival) over the past three decades, both individually and overall. An important change in the past three decades is the increased use of cellular phones. Therefore, we hypothesized that EMSnotification time would have decreased over the timeframe, yielding an overall decrease in EMS response time. Our data are based on the Fatal Accident Reporting System (FARS) using the variables: Time of Crash, EMS Notification Time, EMS Arrival Time, EMS Hospital Arrival Time. This gives a total of 248,981 valid cases following the implementation of our inclusion criteria and truncation of the dataset to the 99th percentile to eliminate unexplainable outliers. We computed the individual and overall median EMS response times for each year from 1987 to 2015. Additionally, we analyzed the response times based on four separate crash factors: weather, total vehicles involved, time of day, and state population density. From 1987 to 2015 the individual EMS response times changed; while notification time has decreased, the arrival at both crash scene and hospital have steadily increased, resulting in overall increased total EMS response time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".