Hospital resources do not predict accuracy of secondary trauma triage: A population-based analysis
Bibliographic record
Abstract
BACKGROUND: The identification of patients who require transfer from non-trauma centers to trauma centers (secondary triage) is complicated by high rates of undertriage and overtriage. The objective of this study was to evaluate variations in secondary triage accuracy across non-trauma centers and identify factors associated with highly accurate secondary triage. METHODS: We performed a population-based study of injured patients who presented to non-trauma centers in a large regional trauma system. Patients were categorized as undertriaged, overtriaged, or appropriately triaged based on transfer status and presence of a severe injury (Injury Severity Score >15, death within 24 hours, or critical injury as defined by the American College of Surgeons). Mixed-effect models, adjusted for case mix and hospital resource, were used to compare triage accuracy across hospitals and identify factors associated with high-performing centers. RESULTS: Among 118,973 patients identified at 182 non-trauma centers, 37,528 (31.5%) had severe injuries. The majority (76.9%) of severely injured patients were not transferred to a trauma center (undertriaged), while 9.6% of nonseverely injured patients were transferred to a trauma center (overtriaged). Mixed-effect models demonstrated that at the average hospital severely injured patients were 3.76 times more likely to be transferred than nonseverely injured patients (diagnostic odds ratio, 3.76; 95% confidence interval, 3.20-4.31). Despite significant variation in triage accuracy across hospitals, adjusted analyses suggested that local resources bore no relationship to triage accuracy. CONCLUSION: Triage accuracy varies significantly across non-trauma centers, after adjusting for hospital resources. These findings suggest that other potentially modifiable factors play a key role in transfer decisions. LEVEL OF EVIDENCE: Therapeutic/care management, level IV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".