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Record W2885900727 · doi:10.1016/j.tjem.2018.06.001

Description of non-urgent patients in the emergency department

2018· article· en· W2885900727 on OpenAlexaboutno aff
Hasan İdil, Turgay Yılmaz Kılıç, Murat Yeşilaras

Bibliographic record

VenueTurkish Journal of Emergency Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentMedical emergencyMedicineBusinessEmergency medicineNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.049
GPT teacher head0.340
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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