A Method for Reviewing the Accuracy and Reliability of a Five-Level Triage Process (Canadian Triage and Acuity Scale) in a Community Emergency Department Setting: Building the Crowding Measurement Infrastructure
Bibliographic record
Abstract
Objectives. Triage data are widely used to evaluate patient flow, disease severity, and emergency department (ED) workload, factors used in ED crowding evaluation and management. We defined an indicator-based methodology that can be easily used to review the accuracy of Canadian Triage and Acuity Scale (CTAS) performance. Methods. A trained nurse reviewer (NR) retrospectively triaged two separate month's ED charts relative to a set of clinical indicators based on CTAS Chief Complaints. Interobserver reliability and accuracy were compared using Kappa and comparative statistics. Results. There were 2838 patients in Trial 1 and 3091 in Trial 2. The rate of inconsistent triage was 14% and 16% (Kappa 0.596 and 0.604). Clinical Indicators "pain scale, chest pain, musculoskeletal injury, respiratory illness, and headache" captured 68% and 62% of visits. Conclusions. We have demonstrated a system to measure the levels of process accuracy and reliability for triage over time. We identified five key clinical indicators which captured over 60% of visits. A simple method for quality review uses a small set of indicators, capturing a majority of cases. Performance consistency and data collection using indicators may be important areas to direct training efforts.
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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 | 0.221 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".