Inter-rater reliability of a computerized presenting-complaint–linked triage system in an urban emergency department
Bibliographic record
Abstract
BACKGROUND: Triage reliability studies typically use hypothetical scenarios and weighted kappa scores where agreement within one level is considered satisfactory. But if triage category is used to help define ED case-mix groups for comparative or benchmarking processes, agreement on exact triage level and major system involved is important. Our hypothesis was that a computerized menu that links presenting complaints to preferred triage levels (PC-linked triage) would provide high triage reliability. OBJECTIVES: Our objective was to assess inter-rater reliability of PC-linked triage using the Canadian Emergency Department Triage and Acuity Scale (CTAS) in a real-time clinical setting, considering agreement on exact triage level and primary body system involved. METHODS: On duty triage nurses entered patient presenting complaint and PC-linked triage level as per standard procedure. In a convenience sample of patients, a second nurse, blinded to triage assignment, observed the triage interaction and independently entered presenting complaint and triage level on a dummy terminal. RESULTS: During the study, 15 nurse pairs triaged 266 patients. Study patients matched actual emergency department case mix closely. Triage nurses agreed exactly in 74% of cases and within one level in 94% of cases. The unweighted kappa value was 0.66 (95% confidence interval [CI], 0.60-0.73) and the quadratic weighted kappa value was 0.75 (95% CI, 0.68-0.81). Kappa for agreement on major system involved was 0.80 (95% CI, 0.69-0.91). CONCLUSION: PC-linked triage has high inter-rater reliability in a real-time clinical setting. PC-linked triage may be useful as one factor in defining case-mix groups for benchmarking and comparative purposes.
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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 | 0.035 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".