Description of non-urgent patients in the emergency department
Bibliographic record
Abstract
Description of non-urgent patients in the emergency departmentThe emergency department (ED) overcrowding is a major public health problem worldwide.One of the important reasons for this is the frequent use of ED by non-urgent patients (1).Crowded EDs negatively impacts the quality of patient care and the satisfaction of patients and staff of the ED (1).For this reason, many studies have been carried out to investigate the characteristics of these patients and the reasons for their choosing the ED.It is important how the "non-urgent patients" are described in the studies.Worldwide accepted criteria for this situation have not yet been established.This important issue should be taken into account when planning studies on this subject.There are some differences on identifying patients among related studies as non-urgent.Patients are usually categorized by a nurse (88%) or a physician, in the triage unit (2).In this phase complaints, vital signs, and waiting times are taken into account (2).In general, patients who do not need urgent intervention and can be treated in primary care units are described as non-urgent (2).Triage levels are helpful in categorizing patients as non-urgent.No special triage category has been identified for non-urgent patients.However, they are often included in the lowest level of urgency (3).It is not difficult to distinguish these patients from urgent patients in crowded EDs where non-urgent patients are treated in an additional unit, known as ''fast-track''.In the triage system used in Turkey, patients are grouped as green, yellow or red starting from the lowest level of urgency.Then, the patients in the yellow and red categories are divided into two subgroups according to their urgency ratings (4).The green (Level 5) category includes patients who are not urgent and can be examined at primary care units or outpatient clinics.This five-level triage system is derived from the Canadian Emergency Department Triage and Acuity Scale (CTAS).It remains uncertain which of the ED patients should be considered as "non-urgent".At this point, it is necessary to determine the objective criteria that can guide.For this purpose extensive literature reviews, additional studies and expert opinions are needed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".