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Record W1892231948 · doi:10.1186/s12245-015-0080-5

Reliability of Canadian Emergency Department Triage and Acuity Scale (CTAS) in Saudi Arabia

2015· article· en· W1892231948 on OpenAlexaffabout
Mustafa Alquraini, Emad Awad, Ra’ed Hijazi

Bibliographic record

VenueInternational Journal of Emergency Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaMcMaster University
FundersKing Abdullah International Medical Research Center
KeywordsMedicineTriageEmergency departmentMedical emergencyReliability (semiconductor)Scale (ratio)Emergency medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The Canadian Emergency Department Triage and Acuity Scale (CTAS) is an integral part of the Canadian emergency medicine triaging system. There is growing interest and implementation of CTAS worldwide. However, little is known about its reliability outside Canada. The aim of this study was to determine the reliability agreement of CTAS in a tertiary care emergency center in Saudi Arabia. METHODS: Ten triage nurses (five senior and five junior nurses) utilized CTAS guidelines to independently assign a triage level for 160 real case-based scenarios. Quadratic weighted kappa statistics were used to measure raters' agreements. RESULTS: Raters provided 1600 triage category assignments to case scenarios for analysis. Intra-rater agreement was similar for both senior and junior nurses; for senior nurses (SN1) kappa 0.871 95 % CI (0.840-0.897), and for junior nurses (SN2) kappa 0.871 95 % CI (0.839-0.898). Inter-rater agreement for the SN1 versus SN2 nurses had statistically meaningful agreement across different triage levels (weighted kappa = 0.770) 95 % CI (0.742-0.797). CONCLUSIONS: CTAS has good reliability among emergency department (ED) triage nurses in King Abdulaziz Medical City (KAMC), Saudi Arabia. The findings suggest that CTAS might be a reliable instrument when applied in countries outside Canada.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.360
Teacher spread0.302 · 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

Citations44
Published2015
Admission routes2
Has abstractyes

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