Comparison of Triage Assessments among Pediatric Registered Nurses and Pediatric Emergency Physicians
Bibliographic record
Abstract
OBJECTIVES: To compare triage level assignments, using simulated written case scenarios, in a pediatric emergency department (ED) among registered nurses (RNs) and pediatric emergency physicians (PEPs) and to compare the triage level assignments among RNs and PEPs with a consensus criterion standard. METHODS: This was a cross-sectional mailed questionnaire survey. The study was conducted at a pediatric tertiary care center with more than 65,000 annual patient visits. Participants were PEPs and RNs working in the ED. Dillman's Total Design Method, with three mailouts, was used for questionnaire construction and implementation. The survey included 55 case scenarios of patients presenting to the ED. Participants were instructed to assign triage level on each case, using the following four-level triage scale: 1 = resuscitation/emergent, 2 = urgent, 3 = less-urgent, and 4 = non-urgent. A priori, all cases were assigned a triage level by consensus agreement of three PEPs, using established triage guidelines from the RNs' teaching manual. Kappa statistics (95% CI) and the mean percentage of correct responses (+/-1 SD) were calculated. RESULTS: There was a 100% response rate (39 RNs, 24 PEPs). The kappa level of agreement (95% CI) was 0.453 (0.447 to 0.459) among the RNs and was 0.419 (0.409 to 0.429) among the PEPs. The mean percentage of correct responses (+/-1 SD) for the RNs was 64.2% (+/-8.0%) and for the PEPs was 53.5% (+/-8.1%, p < 0.01). There was no significant difference within groups by experience level (< 10 vs. > or =10 years) or by the type of work schedule (day vs. evening vs. overnight) or full-time vs. part-time status. CONCLUSIONS: The level of agreement and accuracy of triage assignment was only moderate for both RNs and PEPs. Triage, a crucial step in emergency care, requires improved measurement.
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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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".