Performance of a provincial prehospital trauma triage protocol: A retrospective audit
Bibliographic record
Abstract
Objective To assess the accuracy of a five-step prehospital trauma triage protocol ( Échelle québécoise de triage préhospitalier en traumatologie (EQTPT)) to identify patients requiring urgent and specialized in-hospital trauma care in the Capitale-Nationale region – Québec. Methods The medical records of trauma patients transported by ambulance to one of the five participating emergency departments (EDs) between November 2016 and March 2017 were reviewed. Our primary outcome was the need for one of the following urgent and specialized trauma care: endotracheal intubation in the ED, administration of ≥ 2 blood products in the ED, angioembolization or surgery (excluding single limb surgery) < 24 h and admission to the intensive care unit (ICU) or in-hospital trauma-related death. Results A total of 902 patients were included. The median age was 63 (interquartile range (IQR) 51) and 494 (54.8%) were female. The main trauma mechanism was falls (n = 592), followed by motor vehicle accidents (n = 201). Eighty-two (9.1%) patients required at least one urgent and specialized trauma care. Of those, 44 (53.6%) were identified as requiring transport to a level one trauma centre (steps 1–3), 16 were identified as requiring transport to a centre with a lower level of trauma designation (steps 4–5) while 22 (26.8%) did not meet any of the EQTPT criteria. For steps 1 to 3, the sensitivity was 53.7% (95% confidence interval (CI) 42.9–64.4) and the specificity was 81.7% (95% CI 79.1–84.4) in identifying patients requiring specialized trauma care. Conclusion The EQTPT lacked sensitivity and was poorly specific to identify trauma patients who need specialized in-hospital trauma care.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".