Impact of age on the discriminative ability of an emergency triage system: A cohort study
Bibliographic record
Abstract
BACKGROUND: Emergency triage systems optimize resources in emergency departments (EDs) for those who need urgent care. Five-level triage systems, such as the Canadian Triage and Acuity Scale (CTAS), have been used worldwide. We examined whether the discriminative ability of an emergency triage system varies according to age group using a patient cohort triaged with the Japan Triage and Acuity Scale (JTAS), a validated system based on the CTAS. METHODS: We conducted a cohort study of 27 120 self-presenting patients aged 16 years and older who were triaged with (JTAS) between June 2013 and May 2014 at a Japanese tertiary care hospital. Outcome measures were admission to intensive care units (ICUs) as the primary and in-hospital death as the secondary. We described the trends of the discriminative ability of JTAS using areas under the curve of the receiver operating characteristic (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value of JTAS for seven age categories. RESULTS: The AUROC of JTAS for ICU admission decreased with age (maximum 0.85 to minimum 0.71), sensitivity non-significantly decreased (maximum 0.67 to minimum 0.32), and specificity declined with age (maximum 0.96 to minimum 0.88). The positive and negative predictive value increased (minimum 0.03 to maximum 0.09) and decreased (minimum 0.98 to maximum 0.99), respectively, with age. Overall misclassification increased across age groups (P < 0.001). This trend was mostly consistent with the analysis of in-hospital death. CONCLUSION: Our study suggests that the discriminative ability of an emergency triage system decreases as patient age increases, corresponding to a decrease in specificity. Undertriage may not significantly increase, but misclassification significantly increases as patient age increases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".