First Visit Fallout: Canadian Triage and Acuity Scale (CTAS) and Emergency Department Returns
Bibliographic record
Abstract
Introduction Unplanned return visits (URVs) to the emergency department (ED) within 72 hours are an important quality indicator in emergency medicine, linked to patient safety and the quality of initial care. This study examines whether the Canadian Triage and Acuity Scale (CTAS) category at the initial visit predicts the likelihood of hospital admission upon URV. Methods A retrospective analysis was conducted over a 12-month period at a tertiary care teaching hospital. URVs were defined as registrations within 72 hours of an initial ED discharge, excluding planned returns. Data were extracted from electronic health records, including demographics, CTAS category, disposition, and admission status. Statistical analyses included Pearson correlation, linear regression, and Fisher's exact test to examine relationships between CTAS and admission risk. Statistical significance was set at p < 0.05. Results Of 57,025 ED attendances, 82.1% (46,793) were discharged, of whom 7.6% (3,566) returned within 72 hours. Among URVs, 14.9% (532) resulted in admission. Admission rates on return varied by initial CTAS level, ranging from 23.1% (CTAS 1) to 4.8% (CTAS 5). CTAS 3 patients represented over half of all visits and the highest absolute number of return admissions. A strong negative correlation was observed between CTAS level and URV admission rate (Pearson r = -0.89; p = 0.04). Linear regression confirmed a statistically significant inverse trend, with each one-point increase in CTAS corresponding to a 5.4% absolute reduction in admission rate (R² = 0.90, p = 0.014). Patients triaged as CTAS 1-2 had a relative risk of 1.90 (95% CI: 1.57 to 2.30) for admission on return compared to those triaged as CTAS 3-5. Conclusions The initial CTAS level is a strong predictor of admission following URVs. Stratified analysis revealed that CTAS 3 patients comprise a clinically important group, both in volume and admission risk. These findings support the use of triage-based reporting in ED quality improvement initiatives.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".