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P2‐243: A PROCEDURE FOR IDENTIFYING ITEMS CAPTURING COGNITIVE CHANGE ACROSS TIME: AN ILLUSTRATION USING THE ALZHEIMER'S DISEASE ASSESSMENT SCALE‐COGNITIVE (ADAS‐COG)
Author(s) -
Dowling N.M.,
Bolt Daniel
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2014.05.920
Subject(s) - alzheimer's disease neuroimaging initiative , cognition , cognitive decline , psychology , dementia , disease , neuroimaging , medicine , cognitive impairment , psychiatry , pathology
neuropsychological tests. Results: The Neuro-Norms program takes a case’s demographic data (age, years of education, and sex) and raw scores on neuropsychological tests as input (Figure). It then applies regression equations to express the raw scores as T scores and percentile ranks (these scores express an individual’s standing on the tests, controlling for the effects of age, sex, education and education 2). The number of scores for each case that are classified as abnormally low are provided accompanied by an estimate of the percentage of the normative population that would exhibit that number or more of abnormally low scores (Table). Discrepancy scores between pairs of tests are computed after adjusting for demographic effects and the percentage of the normative population expected to exhibit that number or more of abnormal differences are estimated. Finally, the Mahalanobis Distance Index of the overall abnormality of the case’s profile of scores is provided (including an estimate of the percentage of the normative population expected to exhibit a more unusual profile). Conclusions: This normative study together with the downloadable Neuro-Norms program will provide clinicians and researchers with an accurate and convenient means of analysing a patient’s performance against a large population-based dataset in real-time.