Reliability of Computerized Emergency Triage
Bibliographic record
Abstract
OBJECTIVES: Emergency department (ED) triage prioritizes patients based on urgency of care. This study compared agreement between two blinded, independent users of a Web-based triage tool (eTRIAGE) and examined the effects of ED crowding on triage reliability. METHODS: Consecutive patients presenting to a large, urban, tertiary care ED were assessed by the duty triage nurse and an independent study nurse, both using eTRIAGE. Triage score distribution and agreement are reported. The study nurse collected data on ED activity, and agreement during different levels of ED crowding is reported. Two methods of interrater agreement were used: the linear-weighted kappa and quadratic-weighted kappa. RESULTS: A total of 575 patients were assessed over nine weeks, and complete data were available for 569 patients (99.0%). Agreement between the two nurses was moderate if using linear kappa (weighted kappa = 0.52; 95% confidence interval = 0.46 to 0.57) and good if using quadratic kappa (weighted kappa = 0.66; 95% confidence interval = 0.60 to 0.71). ED overcrowding data were available for 353 patients (62.0%). Agreement did not significantly differ with respect to periods of ambulance diversion, number of admitted inpatients occupying stretchers, number of patients in the waiting room, number of patients registered in two hours, or nurse perception of busyness. CONCLUSIONS: This study demonstrated different agreement depending on the method used to calculate interrater reliability. Using the standard methods, it found good agreement between two independent users of a computerized triage tool. The level of agreement was not affected by various measures of ED crowding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".