The Impact of Distance on Triage to Trauma Center Care in an Urban Trauma System
Bibliographic record
Abstract
BACKGROUND: Urban trauma systems are characterized by high population density, availability of trauma centers, and acceptable road transport times (within 30 minutes). In such systems, patients meeting field trauma triage (FTT) criteria should be transported directly to a trauma center, bypassing closer non-trauma centers. OBJECTIVE: We evaluated emergency medical services (EMS) triage practices to identify opportunities for improving care delivery. OBJECTIVE: Specifically, we evaluated the effect of the additional distance to a trauma center, compared with a closer non-trauma center, on the noncompliance with trauma destination criteria by EMS personnel in an urban environment. METHODS: This was a retrospective cohort study of adults having at least one physiologic derangement and meeting Toronto EMS field trauma triage criteria from 2005 to 2010. Road travel distances between the site of injury, the closest non-trauma center, and the closest trauma center were estimated using geographic information systems. For patients who were transported to non-trauma centers, we estimated "differential distance": the additional travel distance required to transport directly to a trauma center. Logistic regression was used to analyze the effect of differential distance on triage decisions, adjusting for other patient characteristics. RESULTS: Inclusion criteria identified 898 patients; 53% were transported directly to a trauma center. Falls, female gender, and age greater than 65 years were associated with transport to non-trauma centers. Differential distances greater than 1 mile were associated with a decreased likelihood of triage to a trauma center. CONCLUSION: Differential distance between the closest non-trauma center and the closest trauma center was associated with lower compliance with triage protocols, even in an urban setting where trauma centers can be accessed within approximately 30 minutes. Our findings suggest that there are opportunities for reducing the gap between ideal and actual application of field trauma triage guidelines through a process of education and feedback.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".