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Item response theory analysis of the Clinical Dementia Rating.
Li, Yan; Xiong, Chengjie; Aschenbrenner, Andrew J; Chang, Chih-Hung; Weiner, Michael W; Nosheny, Rachel L; Mungas, Dan; Bateman, Randall J; Hassenstab, Jason; Moulder, Krista L; Morris, John C.
Afiliación
  • Li Y; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Xiong C; Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Aschenbrenner AJ; Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Chang CH; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Weiner MW; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Nosheny RL; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Mungas D; Program in Occupational Therapy, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Bateman RJ; Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Hassenstab J; Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Moulder KL; Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
  • Morris JC; San Francisco Veteran's Administration Medical Center, San Francisco, California, USA.
Alzheimers Dement ; 17(3): 534-542, 2021 03.
Article en En | MEDLINE | ID: mdl-33215873
ABSTRACT

INTRODUCTION:

The Clinical Dementia Rating (CDR) is widely used in Alzheimer's disease research studies and has well established reliability and validity. To facilitate the development of an online, electronic CDR (eCDR) for more efficient clinical applications, this study aims to produce a shortened version of the CDR, and to develop the statistical model for automatic scoring.

METHODS:

Item response theory (IRT) was used for item evaluation and model development. An automatic scoring algorithm was validated using existing CDR global and domain box scores as the reference standard.

RESULTS:

Most CDR items discriminate well at mild and very mild levels of cognitive impairment. The bi-factor IRT model fits best and the shortened CDR still demonstrates very high classification accuracy (81%∼92%).

DISCUSSION:

The shortened version of the CDR and the automatic scoring algorithm has established a good foundation for developing an eCDR and will ultimately improve the efficiency of cognitive assessment.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer / Disfunción Cognitiva / Pruebas de Estado Mental y Demencia Tipo de estudio: Prognostic_studies Límite: Aged / Female / Humans / Male Idioma: En Revista: Alzheimers Dement Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer / Disfunción Cognitiva / Pruebas de Estado Mental y Demencia Tipo de estudio: Prognostic_studies Límite: Aged / Female / Humans / Male Idioma: En Revista: Alzheimers Dement Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos