Substantial risk of “Accidental MCI” in healthy older adults: Base rates of low memory scores in neuropsychological assessment
Bibliographic record
Abstract
When assessing older adults for mild cognitive impairment (MCI) or dementia, it is important to understand how often low memory scores are obtained in healthy people in order to minimize false positive diagnoses. This study examines the base rates of low memory scores in older adults across a battery of memory tests. Participants included older adults (55–79 years; N = 742) from the Neuropsychological Assessment Battery (NAB; Stern & White, 2003a) standardization sample. The NAB Memory Module consists of four co-normed memory tests (i.e., List Learning, Shape Learning, Story Learning, and Daily Living Memory) yielding 10 demographically corrected T-scores. When all 10 T-scores were examined simultaneously, 55.5% of older adults had one or more scores one standard deviation (SD) below the mean. At <1.5 SDs, 30.8% of healthy older adults obtained one or more low memory scores. Obtaining low memory scores occurs more often with lesser intellectual abilities. For example, 56.5% of older adults with low average intellectual abilities obtained one or more low memory scores (<1.5 SDs) compared to 21.1% with high average intellectual abilities. Understanding the base rates of low scores can reduce over-interpretation of isolated low memory scores and minimize false positive diagnoses of MCI. (JINS, 2007, 13, 490–500.)The data in Tables 3, 4, 5, 6, and 7 are original data produced by special permission of the Publisher, Psychological Assessment Resources, Inc., 16204 North Florida Avenue, Lutz, Florida 33549, from the standardization data presented in the Neuropsychological Assessment Battery Psychometric and Technical Manual by Travis White, Ph.D. and Robert A. Stern, Ph.D. Copyright 2001, 2003 by PAR, Inc. Further reproduction is prohibited without permission from PAR, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".