71. Factors predicting hospital admission for non-urgent patients triaged with the Canadian Triage and Acuity Scale (CTAS) in the Emergency Department. a retrospective study in Tertiary Center in Makkah, KSA
Bibliographic record
Abstract
Background: Triage system is prioritizing patients according to their urgency to deal with overcrowding of non-urgent patients in the Emergency Department (ED). The aim of this study is to evaluate the admission of non-urgent patients in order to decrease the burden on the ED by triaging them away to the Primary Health Care (PHC). Design and Methods: This retrospective cohort study included all adult non-trauma ED visits in King Abdullah Medical City (KAMC), triaged as Canadian Triage and Acuity Scale (CTAS) IV and V, from May 9 to July 8, 2019. The data was extracted from KAMC database onto SPSS. Multivariate logistic regression was used to examine which factors could affect admission. Results: CTAS IV and CTAS V patients were 30.31% (1495/5066) of total ED visits. Admission was 6.02%; 5.8% for CTAS IV and 0.2% for CTAS V. All CTAS V admissions were elective. Nausea and vomiting (14.4%) were the most frequent chief complaints in the admitted group. The overall referral of non-urgent patients was 12.4% and bounce-back was 13.7%. Logistic regression showed that being tachypneic (OR: 6.68; 95%CI: 1.4-31.5), hypertensive (OR: 3.4; 95%CI: 2.2-5.4) or an oncology patient (OR: 2.85; 95%CI: 1.8-4.6) predicted hospital admission. Conclusion: All CTAS V cases can be safely triaged away to the PHC; CTAS IV can be either triaged away to PHC or to urgent care center, taking into consideration whether the patient is tachypneic, hypertensive or an oncology patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".