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Updated: Jul 15, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Practice effects in mild cognitive impairment: A validation of Calamia et al. (2012)
Kevin Duff1, Dustin B Hammers1
1Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA.
Objective:
In a meta-analysis examining practice effects on repeated neuropsychological testing, Calamia et al. (2012) provided information to predict practice effects in healthy and clinical samples across a range of cognitive domains. However, these estimates have not been validated.
Method:
This study used these prediction estimate calculations to predict follow-up scores across one year on a brief battery of neuropsychological tests in a sample of 93 older adults with amnestic mild cognitive impairment. The predicted follow-up scores were compared to observed follow-up scores.
Results:
Using Calamia et al. model's intercept, age, retest interval, clinical status, and specific cognitive tests, three of the seven observed follow-up scores in this cognitive battery were significantly lower than the Calamia et al. predicted follow-up scores. Differences between individual participants' observed and predicted follow-up scores were more striking. For example, on Delayed Recall of the Hopkins Verbal Learning Test - Revised, 40% of the sample had Calamia et al. predicted scores that were one or more standard deviations above their observed scores. These differences were most notable on tests that were not in Calamia et al.'s cognitive battery, suggesting the meta-analysis results may not generalize as well to other tests.
Conclusions:
Although Calamia et al. provided a method for predicting practice effects and follow-up scores, these results raise caution when using them in MCI, especially on cognitive tests that were not in their meta-analysis.

