Factors predicting hospital admission for non-urgent patients presenting to the emergency department
Bibliographic record
Abstract
Background: The Canadian Triage and Acuity Scale (CTAS) is a scale that identifies the urgency of the case and helps to determine the time needed to be assessed by the physician in the emergency department (ED). However, further research is needed to identify factors that need to be taken into consideration in future CTAS to avoid misclassification of non-urgent patients at high risk who need admission and can be triaged away from the ED. The aim of the study was to evaluate the admission of non-urgent patients to decrease the burden on the ED by triaging them away from primary health care (PHC). Methods: A descriptive-analytical retrospective cohort study was performed including all patients who presented to the ED of King Abdullah Medical Center, Makkah, during a period starting on 9 May 2019 and were classified as CTAS levels 4 and 5. Data of those patients regarding CTAS levels, sex, age, ED visit, vital signs at triage time, pain score, chief complaint, and past medical history extracted from their electronic medical records were entered into the Statistical Package for Social Sciences software (SPSS), and multivariate logistic regression was used to identify predictors of admission. Results: CTAS IV and CTAS V patients accounted for 30.3% (2509/8277) of the total ED visits. The admission rate was 6.1%. Multivariate logistic regression analysis revealed that female patients were 48% less likely to be admitted than males (adjusted odds ratio "AOR": 0.52, 95% confidence interval "CI": 0.36-0.74). Patients who presented with nausea/vomiting had an almost double chance for admission (AOR: 2.03, 95% CI: 1.09-3.79). Patients with a history of hypertension (AOR: 2.39, 95% CI: 1.68-3.40), cancer patients (AOR: 3.02, 95% CI: 2.11-4.32), and patients who presented with a respiratory rate exceeding 20/minute (AOR: 4.88, 95% CI: 1.45- 16.40) were more likely to be admitted than their counterparts. Conclusion: Non-urgent visits to EDs are common practice, and a considerable percentage of patients were admitted. All CTAS V cases can be safely triaged away to the PHC; CTAS IV can be either triaged away to PHC or to the urgent care center taking into consideration whether the patient is tachypneic, hypertensive or an oncology patient.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".