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Record W1503332297

Evaluación de la escala canadiense de "triaje" pediátrico en un servicio de urgencias de pediatría europeo

2010· article· es· W1503332297 on OpenAlexaboutno aff
A. Fernández Landaluce, José Ignacio Pijoán, María Isabel Ares, Santiago Mingegi, Francisco Javier Benito

Bibliographic record

VenueEmergencias · 2010
Typearticle
Languagees
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesTriageMedicinePhilosophyMedical emergency
DOInot available

Abstract

fetched live from OpenAlex

Objetivo: evaluar el funcionamiento y fiabilidad de una version informatizada de la Canadian Paediatric Triage and Acuity Scale (PaedCTAS) en un servicio de urgencias pediatrico (SUP) europeo. Metodo: revision retrospectiva de los episodios de pacientes valorados en el 2007 recogiendo datos sobre nivel de gravedad, estancia media, tasa de ingresos y pruebas complementarias. Evaluamos la fiabilidad comparando la clasificacion de casos realizado por personal de enfermeria (concordancia de su clasificacion y exactitud con respecto a una valoracion de referencia). Resultados: se analizaron 57,617 episodios cuya distribucion por niveles fue la siguiente: nivel I 0.07%; II 1.73%; III 43.1%; IV 50% y V 5.1%. El porcentaje de pruebas radiologicas o de laboratorio realizadas varia desde un 63.6% en el nivel I a un 8% en el nivel V (p En el estudio de fiabilidad la valoracion del triangulo de evaluacion pediatrica fue exacta en el 93.6% de los casos y el indice Kappa fue de 0.77. Se asigno correctamente el nivel de gravedad o incluyo un desacuerdo irrelevante en el 80.3% de los casos. El indice Kappa fue 0.47 (95% 0.46-0.48) Conclusiones: nuestra version informatizada de la PaedCTAS aplicada en un SUP europeo muestra resultados similares a los referidos por los SUP Canadienses. Palabras clave: triage; PaedCTAS; fiabilidad.

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)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient 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 score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.009
GPT teacher head0.304
Teacher spread0.294 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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