The Reliability and Accuracy of International Triage Scale in the Emergency Department (ED): A Literature Review
Bibliographic record
Abstract
A large number of patient visits to the Emergency Department (ED) will influence the outcome of the services provided. The triage scale is one method designed to manage patient screening for quality service improvement. Several triage scales are employed internationally in the EDs including the Australasian Triage Scale (ATS), the Manchester Triage System (MTS), the Canadian Triage and Accuracy Scale (CTAS), and the Emergency Severity Index (ESI). Several studies have a concern to identify the reliability of the triage scale, but only a few of them identified the accuracy of the triage scale. The purpose of this literature review was to identify the best reliability and accuracy among ATS, MTS, CTAS, and ESI based on the literature. The literature search was conducted on electronic databases EBSCO and PubMed with keywords including (triage OR emergency department triage) AND reliability AND ((the Canadian Triage and Accuracy Scale OR CTAS) AND (the Australasian Triage Scale OR ATS) AND (the Manchester Triage System OR MTS) AND the Emergency Severity Index OR ESI)). Assessment of articles was composed based on the PRISMA format with criteria including primary research articles containing the reliability and accuracy of the triage scale in English and published between 2009 – 2019. A total of 271 publications were identified and only 10 studies were included in this literature review. The results reveal that ATS has a moderate level of reliability (k = 04 – 0.57) with an accuracy of 46.2% – 58.3%, CTAS has a good level reliability (k = 0.770) with accuracy of 49%, MTS have good to excellent level of reliability (k = 0.61 – 0.95) with accuracy of 49%, and ESI have moderate to excellent level reliability (k = 0.45 – 0.94) with accuracy of 59.6 % – 72.5%. Based on this review, MTS and ESI are the triage scale with the highest reliability and accuracy. Therefore, MTS and ESI are highly recommended in the ED. However, each EDs need to pay attention to the characteristics, culture, and available resources before choosing and implementing an appropriate triage scale.
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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 | 0.018 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".