Implementation of a Pediatric Emergency Triage System in Xiamen, China
Bibliographic record
Abstract
BACKGROUND: Pediatric emergency rooms (PERs) in Chinese hospitals are perpetually full of sick and injured children because of the lack of sufficiently developed community hospitals and low access to family physicians. The aim of this study was to evaluate the clinical value of a new five-level Chinese pediatric emergency triage system (CPETS), modeled after the Canadian Triage System and Acuity Scale. METHODS: In this study, we compared CPETS outcomes in our PER relative to those of the prior two-level system. Patients who visited our PER before (January 2013-June 2013) and after (January 2014-June 2014) the CPETS was implemented served as the control and experimental group, respectively. Patient flow, triage rates, triage accuracy, wait times (overall and for severe patients), and patient/family satisfaction were compared between the two groups. RESULTS: Relative to the performance of the former system experienced by the control group, the CPETS experienced by the experimental group was associated with a reduced patient flow through the PER (Cox-Stuart test, t = 0, P < 0.05), a higher triage rate (93.40% vs. 90.75%; χ2 = 801.546, P < 0.001), better triage accuracy (96.32% vs. 85.09%; χ2 = 710.904, P < 0.001), shorter overall wait times (37.30 ± 13.80 min vs. 41.60 ± 15.40 min; t = 11.27, P < 0.001), markedly shorter wait times for severe patients (2.07 [0.65, 4.11] min vs. 3.23 [1.90,4.36] min; z = -2.057, P = 0.040), and higher family satisfaction rates (94.23% vs. 92.21%; χ2 = 321.528, P < 0.001). CONCLUSIONS: Implementing the CPETS improved nurses' abilities to triage severe patients and, thus, to deliver the urgent treatments more quickly. The system shunted nonurgent patients to outpatient care effectively, resulting in improved efficiency of PER health-care delivery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".