A Qualitative Assessment of Studies Evaluating the Classification Accuracy of Personnel Using START in Disaster Triage: A Scoping Review
Notice bibliographique
Résumé
Background: Mass casualty incidents (MCIs) can occur as a consequence of a wide variety of events and often require overwhelming prehospital and emergency support and coordinated emergency response. A variety of disaster triage systems have been developed to assist health care providers in making difficult choices with regards to prioritization of victim treatment. The simple triage and rapid treatment (START) triage system is one of the most widely used triage algorithms; however, the research literature addressing real-world or simulation studies documenting the classification accuracy of personnel using START is lacking. Aims and Objectives: To explore the existing literature related to the current state of knowledge about studies assessing the classification accuracy of the START triage system. Design: Scoping review based on Arksey and O'Malley's methodological framework and narrative synthesis based on methods described by Popay and colleagues were performed. Results: The literature search identified 1,820 citations, of which 32 studies met the inclusion criteria. Thirty were peer-reviewed articles and 28 published in the last 10 years (i.e., 2010 and onward). Primary research studies originated in 13 countries and included 3,706 participants conducting triaging assessments involving 2,950 victims. Included studies consisted of five randomized controlled trials, 17 non-randomized controlled studies, eight descriptive studies, and two mixed-method studies. Simulation techniques, mode of delivery, contextual features, and participants' required skills varied among studies. Overall, there was no consistent reporting of outcomes across studies and results were heterogeneous. Data were extracted from the included studies and categorized into two themes: (1) typology of simulations and (2) START system in MCIs simulations. Each theme contains sub-themes regarding the development of simulation employing START as a system for improving individuals' preparedness. These include types of simulation training, settings, and technologies. Other sub-themes include outcome measures and reference standards. Conclusion: This review demonstrates a variety of factors impacting the development and implementation of simulation to assess characteristics of the START system. To further improve simulation-based assessment of triage systems, we recommend the use of reporting guidelines specifically designed for health care simulation research. In particular, reporting of reference standards and test characteristics need to improve in future studies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,042 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».