Accuracy of a Prehospital Triage Protocol in Predicting In-Hospital Mortality and Severe Trauma Cases among Older Adults
Bibliographic record
Abstract
Background: Prehospital trauma triage tools are not tailored to identify severely injured older adults. Our trauma triage protocol based on a three-tier trauma severity grading system (A, B, and C) has never been studied in this population. The objective was to assess its accuracy in predicting in-hospital mortality among older adults (≥65 years) and to compare it to younger patients. Methods: A retrospective multicenter cohort study, from 2011 to 2021. Consecutive adult trauma patients managed by a mobile medical team were prospectively graded A, B, or C according to the initial seriousness of their injuries. Accuracy was evaluated using sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Results: 8888 patients were included (14.1% were ≥65 years). Overall, 10.1% were labeled Grade A (15.2% vs. 9.3% among older and younger adults, respectively), 21.9% Grade B (27.9% vs. 20.9%), and 68.0% Grade C (56.9% vs. 69.8%). In-hospital mortality was 7.1% and was significantly higher among older adults regardless of severity grade. Grade A showed lower sensitivity (50.5 (43.7; 57.2) vs. 74.6 (69.8; 79.1), p < 0.0001) for predicting mortality among older adults compared to their younger counterparts. Similarly, Grade B was associated with lower sensitivity (89.5 (84.7; 93.3) vs. 97.2 (94.8; 98.60), p = 0.0003) and specificity (69.4 (66.3; 72.4) vs. 74.6 (73.6; 75.7], p = 0.001) among older adults. Conclusions: Our prehospital trauma triage protocol offers high sensitivity for predicting in-hospital mortality including older adults.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".