A simple three-step dispatch rule may reduce lights and sirens responses to motor vehicle crashes
Bibliographic record
Abstract
INTRODUCTION: Most patients involved in motor vehicle crashes (MVCs) are not seriously injured. However, dispatch protocols require an ambulance be sent with lights and sirens (L&S) to the vast majority of MVCs. L&S have been shown to reduce response times minimally. The rate of injuries among prehospital workers is nearly 15 times higher among ambulances operating with L&S than those without. OBJECTIVE: To derive a dispatch rule to reduce the need for L&S response by using MVC characteristics that could easily be described by a 9-1-1 caller. The US Centers for Disease Control Field Triage Guidelines were used as the standard for requiring L&S response; it was assumed that if a patient did not require transport to a trauma centre, he/she did not need an L&S response. METHODS: Data were extracted from prehospital patient care reports (PCRs) of patients transported by ambulance to a level I trauma centre between July 2007 and June 2008 with injuries sustained in MVCs. Patients with completed prehospital PCRs and hospital charts were included in the study. Five MVC characteristics were extracted that could easily be identified by a 9-1-1 caller. Using various permutations of these MVC characteristics, a dispatch rule was developed to determine when an ambulance should respond to an MVC without L&S. The sensitivity and specificity of this dispatch rule were calculated for both patients who met trauma centre triage criteria, and those who used trauma centre resources. RESULTS: 509 patients were included in the analysis. The following dispatch rule was developed for an ambulance response without L&S to a MVC: (1) the MVC does not occur on an interstate/highway, (2) and the MVC involves more than one car. AND (3) all patients are ambulatory. This dispatch rule was 95.9% sensitive and 33.5% specific for patients who met trauma centre criteria, and 97.7% sensitive and 32.5% specific for patients who required trauma centre resources. The study was limited by the large number of patients for whom prehospital PCRs were not available. CONCLUSIONS: A simple three-step dispatch rule for MVCs can safely reduce L&S responses by one-third, as judged by need for transport to a trauma centre or use of trauma centre resources. Prospective validation is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.018 | 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".