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Enregistrement W4388519362 · doi:10.1101/2023.11.08.23298265

Comparison between the Smart Triage model and the Emergency Triage Assessment and Treatment (ETAT) guidelines in triaging children presenting to the emergency departments of two public hospitals in Kenya

2023· preprint· en· W4388519362 sur OpenAlexaff
Stephen Kamau, Joyce Kigo, Paul Mwaniki, Dustin Dunsmuir, Yashodani Pillay, Cherri Zhang, Brian Nyamwaya, David Kimutai, Mary Ouma, Ismael Mohammed, Keziah Gachuhi, Mary Chege, Lydia Thuranira, J. Mark Ansermino, Samuel Akech

Notice bibliographique

RevuemedRxiv · 2023
Typepreprint
Langueen
DomaineMedicine
ThématiqueEmergency and Acute Care Studies
Établissements canadiensChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésTriageMedicineMedical emergencyObservational studyEmergency departmentEmergency medicineEmergency medical servicesNursingInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This prospective observational study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment ( ETAT ) guidelines and Smart Triage (ST) models ( ST-only model , ST model with independent triggers , and recalibrated ST model with independent triggers ) in categorizing children into emergency, priority, and non-urgent triage categories. We visualized changes in classification of participants using Sankey diagrams. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified less children, 513 (9.2%), into the emergency category compared to 790 (14.1%), 1163 (20.8%) and 1161 (20.7%) by the ST-only model , ST model with independent triggers and recalibrated model with independent triggers , respectively. ETAT also classified more children, 3089 (55.1%), into the non-urgent triage category compared to 2442 (43.6%), 2097 (37.4%) and 2617 (46.7%) for the respective ST models . ETAT classified 191/395 (48.4%) of admitted patients as emergency compared to more than half by all the ST models . ETAT and the ST-only model classified 25/49 (51%) children who died as emergencies, while the ST models with independent triggers classified 39/49 (79.6%) children as emergencies. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers. Additionally, it performs comparably to ETAT . Evaluation of Smart Triage in other contexts and comparison to other triage systems is required. Author summary Prioritizing children according to the level of severity of illness in the outpatient department is crucial to ensure very sick children are identified and receive life-saving treatment while those with less severe symptoms can safely wait in the queue. Appropriate triage prevents avoidable paediatric mortality. As new triage systems are developed, it is essential to evaluate their performance before being used by healthcare professionals to manage patients. In this study, we compared a newly developed triage algorithm, Smart Triage, to the World Health Organization’s Emergency Triage Assessment and Treatment ( ETAT ) guidelines. Here, we highlight how participants were categorised into emergency, priority, and non-urgent categories by both triage systems. We also assessed changes in triage categorization by comparing the Smart Triage model only (with and without site specific recalibration) and the model with independent emergency and priority triggers aligned with ETAT . Our study shows that Smart Triage had comparable performance to ETAT, and it can be used to triage children in resource-limited settings. Smart Triage can be integrated into a digital device allowing frontline healthcare workers to rapidly triage children presenting to the outpatient department and recognize very sick children faster, so that they can be treated in a timely manner.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,072

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,014
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,130
Tête enseignante GPT0,432
Écart entre enseignants0,303 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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