The prevalence and determinants of non-urgent visits to the Emergency Department in Madinah, Saudi Arabia
Bibliographic record
Abstract
Background: Non-urgent patients' visits to emergency department (ED) is a global concern. In-appropriate ED visits places burden on the ED which limits emergency conditions handling, access to good quality services, raises health care costs and compromise patients' satisfaction. Objective: We aimed to assess prevalence of non-urgent visits to the ED in two main public hos-pitals in Madinah Region namely king Fahad Hospital (KFH) and Madinah General Hospital (MGH). Furthermore, determinants of non-urgent ED visits, such as sociodemographic factors and patients' knowledge on ED were evaluated. Methods: A cross-sectional study was conducted on 280 ED patients attending KFH and MGH. Patients were classified into urgent and non-urgent ED cases according to the Canadian Triage and Acuity Scale (CTAS). Data were collected through a structured interview-based questionnaire covering sociodemographic characteristics, level of triage, knowledge, and preference of ED as well as reasons for ED preference. Results: The overall rate of non-urgent visits to the ED was 55%. Non-urgent visits were signifi-cantly higher among younger age group ≤25 years (81%), singles (70.5%), students (77.6%) and Madinah residents (58.3%) (p
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".