‘Down-triage’ for children with abnormal vital signs: evaluation of a new triage practice at a paediatric emergency department in Japan
Bibliographic record
Abstract
OBJECTIVE: Assessment of abnormal vital signs in triage is a challenge in the paediatric emergency department (PED), since vital signs may reflect anxiety, fever or pain rather than the clinical deterioration of the child. We aimed to evaluate the efficacy of subjective 'down-triage' (change of the initially determined acuity levels) of Japanese Triage and Acuity Scale (JTAS). METHODS: This is a retrospective cohort study of patients in PED up to 15 years of age at a tertiary paediatric medical centre in Japan during a 1-year period. At the end of every JTAS triage process, PED nurses were allowed to 'down-triage' acuity levels of well-appearing patients with abnormal HR or RR, which were presumably attributable to fever, crying or being upset. We compared predictive performance of the triage system before and after 'down-triage' using admission rate as the primary outcome. RESULTS: Among 37 961 PED visits during the study period, we analysed 37 219 records. A total of 17 089 patients (45.9%) were 'down-triaged' after their initial triage allocation upon arrival. Admission rates after 'down-triage' (83%, 33%, 7%, 1% and 3% for levels 1-5, respectively), compared with those of unmodified initial level (16%, 11%, 6%, 2% and 6% for levels 1-5, respectively), had a better apparent relevance with the anticipated admission rates of Canadian Triage and Acuity Scale. CONCLUSIONS: Modification of JTAS through 'down-triage' by experienced staff improves prediction of disposition in a PED. Further research is needed to determine an objective protocol for 'down-triage' to ensure safe practice in a PED.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".