Effectiveness of a five-level Paediatric Triage System: an analysis of resource utilisation in the emergency department in Taiwan
Bibliographic record
Abstract
OBJECTIVES: To examine the effectiveness of a five-level Paediatric Triage and Acuity System (Ped-TTAS) by comparing the reliability of patient prioritisation and resource utilisation with the four-level Paediatric Taiwan Triage System (Ped-TTS) among non-trauma paediatric patients in the emergency department (ED). METHODS: The study design used was a retrospective longitudinal analysis based on medical chart review and a computer database. Except for a shorter list of complaints and some abnormal vital sign criteria modifications, the structure and triage process for applying Ped-TTAS was similar to that of the Paediatric Canadian Emergency Triage and Acuity Scale. Non-trauma paediatric patients presenting to the ED were triaged by well-trained triage nurses using the four-level Ped-TTS in 2008 and five-level Ped-TTAS in 2010. Hospitalisation rates and medical resource utilisation were analysed by acuity levels between the contrasting study groups. RESULTS: There was a significant difference in patient prioritisation between the four-level Ped-TTS and five-level Ped-TTAS. Improved differentiation was observed with the five-level Ped-TTAS in predicting hospitalisation rates and medical costs. CONCLUSIONS: The five-level Ped-TTAS is better able to discriminate paediatric patients by triage acuity in the ED and is also more precise in predicting resource utilisation. The introduction of a more accurate acuity and triage system for use in paediatric emergency care should provide greater patient safety and more timely utilisation of appropriate ED resources.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".