Modern Triage in the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Because the volume of patient admissions to an emergency department (ED) cannot be precisely planned, the available resources may become overwhelmed at times ("crowding"), with resulting risks for patient safety. The aim of this study is to identify modern triage instruments and assess their validity and reliability. METHODS: Review of selected literature retrieved by a search on the terms "emergency department" and "triage." RESULTS: Emergency departments around the world use different triage systems to assess the severity of incoming patients' conditions and assign treatment priorities. Our study identified four such instruments: the Australasian Triage Scale (ATS), the Canadian Triage and Acuity Scale (CTAS), the Manchester Triage System (MTS), and the Emergency Severity Index (ESI). Triage instruments with 5 levels are superior to those with 3 levels in both validity and reliability (p<0.01). Good to very good reliability has been shown for the best-studied instruments, CTAS and ESI (κ-statistics: 0.7 to 0.95), while ATS and MTS have been found to be only moderately reliable (κ-statistics: 0.3 to 0.6). MTS and ESI are both available in German; of these two, only the ESI has been validated in German-speaking countries. CONCLUSION: Five-level triage systems are valid and reliable methods for assessment of the severity of incoming patients' conditions by nursing staff in the emergency department. They should be used in German emergency departments to assign treatment priorities in a structured and dependable fashion.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".