Analyzing the Usability of the 5-Level Canadian Triage and Acuity Scale By Paramedics in the Prehospital Environment
Bibliographic record
Abstract
Introduction: ED crowding negatively affects throughput, quality of care, and outcomes. Paramedics do not have an evidence-based, feasible triage instrument to guide classification of patients. No studies have compared the Canadian Triage and Acuity Scale (CTAS) used by prehospital paramedics against the Emergency Severity Index (ESI) used by nurses in the emergency department. This study sought to determine if a relationship exists between paramedics’ triage scores and emergency nurses’ scores in the emergency department using 2 common 5-level triage instruments, as well as to determine whether either instrument correlates with patient admission. Methods: CTAS scores determined by paramedics on arrival at the emergency department were compared with the initial ESI scores determined by emergency nurses. Both scores were compared with the patient’s disposition status. Data analyses included descriptive statistics, ?2 statistics, and hierarchical regression analysis. Results: The analysis included 2,222 patients. There was a poor relationship between the CTAS and the ESI at the facility (P = .599, ? = –0.003). The final regression model explained 32.9% of the admission variance (P < .001). The model correctly predicted 61.5% of admissions, with an 82% accuracy rate for all other forms of disposition and an overall model prediction rate of 73.7%. Discussion: Using the CTAS, paramedics can predict admission comparably with nurses using the ESI. However, both instruments showed weakness in over- and under-triage rates. Additional studies are indicated to better understand prehospital paramedic triage and its impact on throughput.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".