Feasibility and optimization of a second-tier prehospital critical care response for major trauma in a North American urban and suburban area: A geospatial analysis and modelling study
Bibliographic record
Abstract
OBJECTIVE: Prehospital Critical Care Response Units (CCRUs) dispatched to the scene of major traumas can deliver advanced interventions at scene but are uncommon in North America. We sought to evaluate the feasibility of CCRU response to major trauma in a North American urban-suburban region. METHODS: We obtained ambulance record-level data from three paramedic services in Ontario, Canada (Toronto Paramedic Service, Peel Regional Paramedic Service, and Halton Region Paramedic Service) from January 2018 to December 2022 which we aggregated into calls and applied inclusion criteria targeting major trauma. We used mathematical modelling to determine the optimal placement of CCRU bases containing an RRV or RRV/helicopter for trauma response and evaluated their expected counterfactual coverage performance using simulation. Our primary metrics were the expected number of major traumas that could have been reached by CCRUs prior to EMS departure from the scene and the resulting expected average reduction in time to accessing critical care for those patients. RESULTS: We found the expected counterfactual coverage of two optimally placed RRV teams to be 80 % (N = 5092) of 6391 major trauma calls included. This corresponded to an expected average reduction in time to critical care of 30 min (from 47 to 17 min). We found only marginal improvement in total calls reached by CCRUs when an RRV team was replaced with an RRV/helicopter team. CONCLUSIONS: Our analysis supports the feasibility of CCRU response to major trauma in a North American mixed urban-suburban region and motivates further investigation into CCRUs' clinical and cost effectiveness.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".