Optimizing Trauma Systems
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop a data-driven approach to assessing the influence of trauma system parameters and optimizing the configuration of the Victorian State Trauma System (VSTS). SUMMARY BACKGROUND DATA: Regionalized trauma systems have been shown to reduce the risk of mortality and improve patient function and health-related quality of life. However, major trauma case numbers are rapidly increasing and there is a need to evolve the configuration of trauma systems. METHODS: A retrospective review of major trauma patients from 2016 to 2018 in Victoria, Australia. Drive times and flight times were calculated for transport to each of 138 trauma receiving hospitals. Changes to the configuration of the VSTS were modeled using a Mixed Integer Linear Programming algorithm across 156 simulations. RESULTS: There were 8327 patients included in the study, of which 58% were transported directly to a major trauma service (MTS). For adult patients, the proportion of patients transported directly to an MTS increased with higher transport time limit, greater probability of helicopter emergency medical service utilization, and lower hospital patient threshold numbers. The proportion of adult patients transported directly to an MTS varied from 66% to 90% across simulations. Across all simulations for pediatric patients, only 1 pediatric MTS was assigned. CONCLUSIONS: We have developed a robust and data-driven approach to optimizing trauma systems. Through the use of geospatial and mathematical models, we have modeled how potential future changes to trauma system characteristics may impact on the optimal configuration of the system, which will enable policy makers to make informed decisions about health service planning into the future.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".