Predictors of Undertriage after Major Trauma in a Physician-led Prehospital System: Insights of a French Registry
Bibliographic record
Abstract
Abstract Background The proper prehospital triage and transportation of patients suffering major trauma to lever 1 trauma centers is associated with better outcomes. Hence, emergency medical systems (EMS) aim is to avoid undertriage in these patients. The main objective of this study was to assess the rate and predictors of undertriage in a physician-led prehospital system. Methods We conducted an observational multicentric, region-wide, retrospective study based on the RESUVal Trauma-System registry, Rhône-Alpes region, France. All adults assessed by physician-led EMS units, from January 2011 to December 2017 with major trauma (Injury Severity Score (ISS) ≥ 16) were included. We defined the correct-triage group as major trauma patients admitted to a level I trauma center. We performed univariate then multivariate logistic regressions with undertriage as outcome. Results A total of 7,110 patients were included in the registry, of whom 2,591 patients with an ISS≥ 16. Among these patients, 320 (12.35%) were undertriaged. Median ISS was 25. In-hospital mortality was 16.45% (n=351/2134). Mid-aged patients (51-65 years old) were associated with a higher risk of undertriage than the others (OR=1.62, 95%CI 1.15-2.28, p=0.01). Factors associated with a lower risk of undertriage were: mechanism (fall or gunshot/stabbing wounds, 0.62, [0.45-0.86], p=0.01 and 0.44, [0.22-0.9], p=0.02, respectively), time on-scene (over 60 minutes, 0.61, [0.38-0.95], p=0.03), prehospital need for endotracheal intubation and ultrasound examination (0.53, [0.39-0.72], p<0.001 and 0.15, [0.08-0.29], p<0.001 respectively). After adjusting for severity, undertriage showed a non-significant tendency toward an increased risk of mortality (1.22, [0.8-1.89], p=0.36). Conclusions In our region-wide, physician-led prehospital system, undertriage of major trauma was not rare. The typical profile of undertriaged patients was a mid-aged male suffering from a blunt trauma, without respiratory distress or neurologic impairment, not benefiting from prehospital ultrasound examination and located close to a non-trauma center hospital.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.007 |
| 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".