Cognitive Change Checklist: Cross-Validation in an ADRC Sample

2009 ◽  
Author(s):  
John A. Schinka ◽  
Ashok Raj ◽  
David A. Loewenstein
Keyword(s):  
Cross Validation ◽  
Cognitive Change

scholarly journals Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI

2020 ◽  
Author(s):  
Binyin Li ◽  
Miao Zhang ◽  
Joost Riphagen ◽  
Kathryn Morrison Yochim ◽  
Biao Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Cross Validation ◽  
Cognitive Change ◽  
Structural Features ◽  
Age Effects ◽  
Validation Dataset

Abstract Background: Structural neuroimaging has been applied towards identification of individuals with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. Therefore, careful consideration of age effects in the modeling of AD degenerative patterns could provide more sensitive detection of the earliest stages of brain disease.Methods: We built linear models for age based on multiple combined structural features (cortical thickness, subcortical structural volumes, ratio of gray to white matter signal intensity, white matter signal abnormalities, total intracranial volume) in 272 healthy adults across a wide age range (D1: age 36-108). These models were then used to create a new support vector machine (SVM) training model with 10-fold cross validation in 136 AD and 268 control participants (D2) based on deviations from the expected age-effects found in the initial sample. Subsequent validation assessed the accuracy of the SVM model to correctly classify AD patients in a new dataset (D3). Finally, we applied the classifier to individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change.Results: Optimal cross-validation accuracy was 93.07% in the D2, compared to 91.83% without age detrending in D1. In the validation dataset (D3), the classifier obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). In the MCI dataset, we observed significantly greater longitudinal cognitive decline in MCI who were classified as more ‘AD-like’ (MCI-AD), and this effect was pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right medial temporal & parahippocampus gyrus.Conclusions: Linear detrending for age in SVM for combined structural features resulted in good performance for classification of AD and generalization of MCI prediction. Such procedures should be employed in future work.

scholarly journals The cognitive change checklist (3CL): cross-validation of a measure of change in everyday cognition

2010 ◽  
Vol 25 (3) ◽  
pp. 266-274 ◽  
Author(s):  
John A. Schinka ◽  
Ashok Raj ◽  
David A. Loewenstein ◽  
Brent J. Small ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Cross Validation ◽  
Cognitive Change ◽  
Everyday Cognition

Issues of Capacity and Decision-Making in the Context of Delirium

2015 ◽  
Vol 24 (4) ◽  
pp. 140-145
Author(s):  
Kevin R. Patterson
Keyword(s):  
Decision Making ◽  
Patient Autonomy ◽  
Care Setting ◽  
Cognitive Change ◽  
Treatment Team ◽  
Precipitating Factors ◽  
Term Care ◽  
Decision Making Capacity ◽  
Fundamental Consideration

Decision-making capacity is a fundamental consideration in working with patients in a clinical setting. One of the most common conditions affecting decision-making capacity in patients in the inpatient or long-term care setting is a form of acute, transient cognitive change known as delirium. A thorough understanding of delirium — how it can present, its predisposing and precipitating factors, and how it can be managed — will improve a speech-language pathologist's (SLPs) ability to make treatment recommendations, and to advise the treatment team on issues related to communication and patient autonomy.

Item Bias Analysis of the Young Schema-Questionnaire for Psychopathology, Gender, and Educational Level

2011 ◽  
Vol 27 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Marleen M. Rijkeboer ◽  
Huub van den Bergh ◽  
Jan van den Bout
Keyword(s):  
Educational Level ◽  
Cross Validation ◽  
Item Bias ◽  
Response Patterns ◽  
Dutch Population ◽  
Bias Analysis ◽  
Young Schema Questionnaire ◽  
Item Functioning ◽  
Item Level ◽  
Schema Questionnaire

This study examines the construct validity of the Young Schema-Questionnaire at the item level in a Dutch population. Possible bias of items in relation to the presence or absence of psychopathology, gender, and educational level was analyzed, using a cross-validation design. None of the items of the YSQ exhibited differential item functioning (DIF) for gender, and only one item showed DIF for educational level. Furthermore, item bias analysis did not identify DIF for the presence or absence of psychopathology in as much as 195 of the 205 items comprising the YSQ. Ten items, however, spread over the questionnaire, were found to yield relatively inconsistent response patterns for patients and nonclinical participants.

CROSS-VALIDATION IS NEEDED

1972 ◽  
Vol 17 (2) ◽  
pp. 85-86
Author(s):  
RICHARD F. Q. JOHNSON
Keyword(s):  
Cross Validation

Cubist Portrait of a Cognitive Change

1979 ◽  
Vol 24 (8) ◽  
pp. 645-646
Author(s):  
SUSAN F. CHIPMAN
Keyword(s):  
Cognitive Change

Do cognitive change strategies matter in cognitive therapy?

2000 ◽  
Vol 3 (1) ◽  
Author(s):  
Steven D. Hollon
Keyword(s):  
Cognitive Therapy ◽  
Cognitive Change ◽  
Change Strategies
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