Emergency Medical Services Response Time and Mortality in an Urban Setting
Bibliographic record
Abstract
BACKGROUND: A common tenet in emergency medical services (EMS) is that faster response equates to better patient outcome, translated by some EMS operations into a goal of a response time of 8 minutes or less for advanced life support (ALS) units responding to life-threatening events. OBJECTIVE: To explore whether an 8-minute EMS response time was associated with mortality. METHODS: This was a one-year retrospective cohort study of adults with a life-threatening event as assessed at the time of the 9-1-1 call (Medical Priority Dispatch System Echo- or Delta-level event). The study setting was an urban all-ALS EMS system serving a population of approximately 1 million. Response time was defined as 9-1-1 call receipt to ALS unit arrival on scene, and outcome was defined as all-cause mortality at hospital discharge. Potential covariates included patient acuity, age, gender, and combined scene and transport interval time. Stratified analysis and logistic regression were used to assess the response time-mortality association. RESULTS: There were 7,760 unit responses that met the inclusion criteria; 1,865 (24%) were ≥8 minutes. The average patient age was 56.7 years (standard deviation = 21.5). For patients with a response time ≥8 minutes, 7.1% died, compared with 6.4% for patients with a response time ≤7 minutes 59 seconds (risk difference 0.7%; 95% confidence interval [CI]: -0.5%, 2.0%). The adjusted odds ratio of mortality for ≥8 minutes was 1.19 (95% CI: 0.97, 1.47). An exploratory analysis suggested there may be a small beneficial effect of response ≤7 minutes 59 seconds for those who survived to become an inpatient (adjusted odds ratio = 1.30; 95% CI: 1.00, 1.69). CONCLUSIONS: These results call into question the clinical effectiveness of a dichotomous 8-minute ALS response time on decreasing mortality for the majority of adult patients identified as having a life-threatening event at the time of the 9-1-1 call. However, this study does not suggest that rapid EMS response is undesirable or unimportant for certain patients. This analysis highlights the need for further research on who may benefit from rapid EMS response, whether these individuals can be identified at the time of the 9-1-1 call, and what the optimum response time is.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".