Clinical Considerations When Applying Vital Signs in Pediatric Korean Triage and Acuity Scale
Bibliographic record
Abstract
Initial vital signs of children at the emergency department may be abnormal because of anxiety and irritability, resulting in unrealistic triage levels. This study aimed to evaluate the effectiveness of pediatric triage by clinical decision based on the patient's general condition. The Pediatric Korean Triage and Acuity Scale (PedKTAS) has been used nationwide for triage since 2016. The triage level, as assessed by an experienced triage nurse and based on the patient's clinical condition, was defined as the 'real practice (RP)-level,' while the re-calculated triage level, as assessed by the direct application of initial vital signs, was defined as the 'simulation (S)-level.' A total of 22,841 patients were triaged during the study period. The hospitalization rate according to RP-PedKTAS levels exhibited a significant correlation with the expected hospitalization rate suggested by the Pediatric Canadian Triage and Acuity Scale (CTAS) (P = 0.002), whereas the S-PedKTAS levels did not (P = 0.151). Compared with the previously reported pediatric CTAS level-specific hospitalization rate and intensive care unit (ICU) admission rate, RP-PedKTAS was significantly correlated with both hospitalization rate and ICU admission rate (P = 0.001 and P = 0.012, respectively). However, S-PedKTAS showed no significant correlation in both (P = 0.267 and P = 0.188, respectively). The determination of triage levels based on clinical decision rather than the direct application of abnormal initial vital signs to PedKTAS is more accurate in predicting the hospitalization rate and ICU admission rate.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".