A novel methodology to characterize interfacility transfer strategies in a trauma transfer network
Bibliographic record
Abstract
BACKGROUND: More than half of severely injured patients are initially transported from the scene of injury to nontrauma centers (NTCs), with many requiring subsequent transfer to trauma center (TC) care. Definitive care in the setting of severe injury is time sensitive. However, transferring severely injured patients from an NTC is a complex process often fraught with delays. Selection of the receiving TC and the mode of interfacility transport both strongly influence total transfer time and are highly amenable to quality improvement initiatives. METHODS: We analyzed transfer strategies, defined as the pairing of a destination and mode of transport (land vs. rotary wing vs. fixed wing), for severely injured adult patients. Existing transfer strategies at each NTC were derived from trauma registry data. Geographic Information Systems network analysis was used to identify the strategy that minimized transfer times the most as well as alternate strategies (+15 or +30 minutes) for each NTC. Transfer network efficiency was characterized based on optimality and stability. RESULTS: We identified 7,702 severely injured adult patients transferred from 146 NTCs to 9 TCs. Nontrauma centers transferred severely injured patients to a median of 3 (interquartile range, 1-4) different TCs and utilized a median of 4 (interquartile range, 2-6) different transfer strategies. After allowing for the use of alternate transfer strategies, 73.1% of severely injured patients were transported using optimal/alternate strategies, and only 40.4% of NTCs transferred more than 90% of patients using an optimal/alternate transfer strategy. Three quarters (75.5%) of transfers occurred between NTCs and their most common receiving TC. CONCLUSION: More than a quarter of patients with severe traumatic injuries undergoing interfacility transport to a TC in Ontario are consistently transported using a nonoptimal combination of destination and mode of transport. Our novel analytic approach can be easily adapted to different system configurations and provides actionable data that can be provided to NTCs and other stakeholders. LEVEL OF EVIDENCE: Therapeutic study, level IV.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".