Pre-hospital trauma triage: Outcomes of interfacility transferred trauma patients meeting pre-hospital triage criteria
Bibliographic record
Abstract
Introduction In Ontario, Canada, paramedics use the Field Trauma Triage Standard to identify patients at risk for severe injury. These triage criteria encompass physiologic, anatomic, mechanism of injury, and special considerations to identify patients that should be transported directly to a trauma center. Patients meeting any one of these criteria mandate direct transfer to a trauma center. This study evaluated whether severely injured trauma patients that underwent an interfacility transfer met these triage criteria. The secondary objective was to assess the impact of failed triage application on in-hospital mortality. Methods This is a retrospective cohort study of interfacility trauma transfers to an adult trauma center over a 3-year period that were either admitted to the intensive care unit, received an operation within 4 h of arrival, or died within 48 h of arrival. Data were abstracted from the hospital’s trauma registry and chart review of electronic medical records. Frequency of patients meeting pre-hospital triage criteria and which specific criteria were collected. Multivariable logistic regression was performed to assess the impact of missed pre-hospital triage on in-hospital mortality. Results There were 1008 interfacility patients during the study period, of which 340 patients met inclusion criteria; 78.5% ( n = 267) of interfacility transports had met at least one triage criteria. Most frequent criteria met were: Glasgow Coma Scale <14 (42.4%), high risk motor-vehicle collision (22.1%), and systolic blood pressure <90 mmHg (19.4%). When adjusted for injury severity score and age, patients who met triage criteria were not at increased odds of death (OR 2.38, 95% CI: 0.87–6.46) compared to interfacility patients that did not meet criteria. Conclusion: A majority of critically injured interfacility transfers met initial trauma triage criteria. These patients are at high risk for preventable morbidity and mortality. This study indicates the need to understand the barriers to pre-hospital adherence to trauma triage guidelines.
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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 | 0.000 | 0.004 |
| 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".