Exploring the Predictors of Emergency Department Triage Acuity Assignment in Patients With Sepsis
Bibliographic record
Abstract
Background and purpose Evidence suggests that septic patients, who require prompt medical attention, may be undertriaged, resulting in delayed treatment. The purpose of this study was to examine patient and contextual variables that contribute to high- versus low-acuity triage classification of patients with sepsis. Methods Data were abstracted from the medical records of 154 adult patients with sepsis admitted to hospital through a Canadian Emergency Department. Logistic regression was used to explore the predictors of triage classification. Results Language barriers or chronic cognitive impairment (odds ratio 5.7; 95% confidence interval 2.15, 15.01), acute confusion (odds ratio 3.4; confidence interval 1.3, 8.2), unwell appearance (odds ratio 3.4; 95% confidence interval 1.7, 7.0), and hypotension (odds ratio 0.98; confidence interval 0.96, 1.0) were predictive of higher acuity classification. Temperature, heart rate, respiratory rate, and contextual factors were not related to triage classification. Conclusions Several patient-related factors were related to triage classification. However, the finding that temperature and heart and respiratory rates were not related to triage classification was troubling. Our findings point to a need for enhanced education for triage nurses regarding the physiological indices of sepsis. The sensitivity of the Canadian Triage Assessment Scale, used in Canadian Emergency Rooms, also needs to be examined.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".