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Age Corrections and Dementia Classification Accuracy
Author(s) -
Megan E. O’Connell,
Holly Tuokko
Publication year - 2010
Publication title -
archives of clinical neuropsychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.909
H-Index - 98
eISSN - 1873-5843
pISSN - 0887-6177
DOI - 10.1093/arclin/acp111
Subject(s) - dementia , raw score , percentile , normative , cutoff , raw data , contrast (vision) , diagnostic accuracy , medicine , statistics , audiology , psychology , mathematics , artificial intelligence , computer science , disease , philosophy , physics , epistemology , quantum mechanics
In contrast to expectations, demographic corrections to reduce biases against those of advanced age or few years of education does not universally improve diagnostic classification accuracy. Age corrections may be particularly problematic because age is also a risk factor for a dementia diagnosis. We found that simulating increased risk for dementia based on demographic variables, such as age, reduced the overall classification accuracy for demographically corrected simulated scores relative to the raw, uncorrected test scores. In clinical data with a small magnitude of association between age and dementia diagnosis, we found equivalent overall classification accuracy for demographically corrected and raw test scores. Regardless of the overall classification accuracy results, cutoff comparisons (16th and 9th percentiles) in clinical and simulated data demonstrated that for the most part, the sensitivity of raw scores was higher than the sensitivity of demographically corrected scores, but the specificity of scores corrected with normative data was superior.

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