Emergency Department triage systems
Bibliographic record
Abstract
Introduction: In recent decades, it is observed a significant increase in the number of patients who visit Emergency Departments (ED). Therefore, it is imperative to adopt an effective triage system. Aim: The aim of the present systematic review was to explore the available Triage Systems for EDs. Material and Method: A systematic literature review in international databases (PubMed and Scopus) between January 2017 and April 2017. During the initial research 3462 articles were indetified, however only 11 met the inclusion criteria.Results: The most common triage systems in Emergency Departments are the Australasian Triage Scale, the Canadian Emergency Department Triage and Acuity Scale (CTAS), whereas the use of the Manchester Triage System is limited mainly due to its complexity. The application of the Emergency Severity Index (ESI) tends to be increased because of its advantages compared to other systems. It is important to mention that no study has been found in Greece to explore the triage system in ED.Conclusions: There are many scales and systems available for the effective triage in EDs and nurses play significant role in their implementation. Moreover, it is important the authorities to establish legislation in Greece so as effective triage systems to be applied in Greek hospitals.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".