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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
Published on: June 25, 2019
Symptom validity indicators embedded in the Controlled Oral Word Association Test
Season C Johnson1, Noah D Silverberg, Scott R Millis
1Department of Psychology, University of British Columbia, Vancouver, BC, Canada.
The Clinical Neuropsychologist
|August 4, 2012
Summary
This study identified the best embedded symptom validity measures for the Controlled Oral Word Association Test (COWAT) to detect malingered neurocognitive dysfunction (MND). Combining COWAT total score and change in output effectively differentiates patients with and without MND.
Area of Science:
- Neuropsychology
- Cognitive Assessment
- Forensic Psychology
Background:
- Embedded symptom validity measures (SVMs) are crucial for detecting suboptimal performance in neuropsychological assessments.
- The Controlled Oral Word Association Test (COWAT) is a common test for word generation, but optimal SVMs for it are unclear.
- Previous SVMs for COWAT lack cross-validation, necessitating research into the best predictive combinations.
Purpose of the Study:
- To identify the optimal combination of embedded symptom validity measures for the Controlled Oral Word Association Test (COWAT).
- To differentiate between patients with and without malingered neurocognitive dysfunction (MND) using COWAT indicators.
- To validate the effectiveness of selected COWAT SVMs in a clinical population, particularly mild traumatic brain injury (mTBI).
Main Methods:
- Bayesian Model Averaging (BMA) was employed to select the most effective predictors from embedded COWAT symptom validity measures.
- A cohort of patients with (n=46) and without (n=55) suspected MND was analyzed.
- Logistic regression was used to evaluate the discriminative ability of the selected predictors, calculating AUC, sensitivity, and specificity.
Main Results:
- Bayesian Model Averaging identified the COWAT total score and a measure of output change over time as optimal predictors.
- A logistic regression model combining these variables achieved an AUC of .774, correctly classifying 78% of cases.
- The model demonstrated good discriminability with 67% sensitivity and 88% specificity, outperforming alternatives with longer administration times.
Conclusions:
- A weighted combination of embedded symptom validity measures, specifically COWAT total score and change in output, effectively detects malingered neurocognitive dysfunction (MND).
- These findings support the practical application of these validated COWAT indicators in clinical and forensic neuropsychological evaluations.
- The selected measures offer a time-efficient and accurate method for identifying patients who may not be performing at their cognitive capacity.
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