Assessing the Impact of Waiting Time on Triage Color Code Assignment and One‐Year Mortality in the Emergency Department: A Causal Mediation Analysis
Bibliographic record
Abstract
Background and Aims: Emergency Department (ED) overcrowding and delays in care affect patient outcomes. While triage systems prioritize care based on urgency, the role of waiting time in mediating the relationship between triage color codes and 1-year mortality remains unclear. This study investigates this mediation effect to improve triage protocols and patient outcomes. Methods: A retrospective cohort study was conducted using data from the Fondazione Policlinico Universitario Agostino Gemelli IRCCS ED (2014-2018). The sample included patients assigned green and yellow triage codes, excluding red and white ones. The outcome was 1-year mortality; the mediator was waiting time, defined as the delay between triage assignment and medical evaluation. Causal mediation analysis estimated direct, indirect, and total effects, with sensitivity analyses assessing robustness to unmeasured confounding. Results: Among 56,284 observations, older patients and yellow-coded individuals showed higher 1-year mortality. Waiting time did not significantly mediate the relationship between triage code and mortality (ACME OR: 1.001, 95% CI: 0.999-1.002). Triage code, however, had a direct significant effect on mortality (ADE OR: 1.01, 95% CI: 1.004-1.007). Waiting time mediated a small proportion of the effect (3.4%-13.9%). Sensitivity analyses indicated the mediation effect was sensitive to unmeasured confounding. Conclusions: Triage color code strongly predicts 1-year mortality, independent of waiting time within standard thresholds. For lower-acuity cases, reducing waiting time further may not improve long-term outcomes. Future research should validate these findings across multicenter settings and explore Italy's updated five-color triage system to optimize care delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".