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Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing.
Zorn, Claudine; Bauer, Eric; Feffer, Marie-Laurence; Moerschel, Elisabeth; Bierry, Guillaume; Choquet, Philippe; Dillenseger, Jean-Philippe.
Afiliação
  • Zorn C; Section Imagerie Médicale et Radiologie Thérapeutique, Lycée Jean Rostand, Académie de Strasbourg, Strasbourg, France; Comité scientifique de l'Association Française du Personnel Paramédical d'Electroradiologie Médicale (AFPPE), Montrouge, Paris, France.
  • Bauer E; Section Imagerie Médicale et Radiologie Thérapeutique, Lycée Jean Rostand, Académie de Strasbourg, Strasbourg, France.
  • Feffer ML; Section Imagerie Médicale et Radiologie Thérapeutique, Lycée Jean Rostand, Académie de Strasbourg, Strasbourg, France.
  • Moerschel E; Section Imagerie Médicale et Radiologie Thérapeutique, Lycée Jean Rostand, Académie de Strasbourg, Strasbourg, France.
  • Bierry G; Pôle d'imagerie médicale, Hôpital de Hautepierre, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France.
  • Choquet P; Pôle d'imagerie médicale, Hôpital de Hautepierre, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France.
  • Dillenseger JP; Section Imagerie Médicale et Radiologie Thérapeutique, Lycée Jean Rostand, Académie de Strasbourg, Strasbourg, France; Comité scientifique de l'Association Française du Personnel Paramédical d'Electroradiologie Médicale (AFPPE), Montrouge, Paris, France; Pôle d'imagerie médicale, Hôpital de Hautepie
J Med Imaging Radiat Sci ; 51(1): 173-181, 2020 03.
Article em En | MEDLINE | ID: mdl-32057745
INTRODUCTION: This study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest. MATERIALS AND METHODS: This study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition. RESULTS: The learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1. DISCUSSION: The measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them. CONCLUSION: Obtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Interpretação de Imagem Radiográfica Assistida por Computador / Tomografia Computadorizada por Raios X / Tecnologia Radiológica / Competência Clínica / Curva de Aprendizado Limite: Humans País/Região como assunto: Europa Idioma: En Revista: J Med Imaging Radiat Sci Ano de publicação: 2020 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Interpretação de Imagem Radiográfica Assistida por Computador / Tomografia Computadorizada por Raios X / Tecnologia Radiológica / Competência Clínica / Curva de Aprendizado Limite: Humans País/Região como assunto: Europa Idioma: En Revista: J Med Imaging Radiat Sci Ano de publicação: 2020 Tipo de documento: Article País de afiliação: França