Recognition Memory and Verbal Fluency Differentiate Probable Alzheimer Disease From Subcortical Ischemic Vascular Dementia
Bibliographic record
Abstract
BACKGROUND: Alzheimer disease (AD) and vascular dementia are among the most frequently occurring causes of dementia in the world, and their accurate differentiation is important because different pharmaceutical strategies may modify the course of each disease. OBJECTIVE: To determine which of 10 neuropsychological test scores can accurately differentiate patients with probable AD from those with subcortical ischemic vascular dementia (SIVD) for use in evidence-based clinical practice. DESIGN: Patients with suspected dementia were referred to the study by family physicians, geriatricians, and neurologists. All participants received a thorough assessment according to standard diagnostic guidelines. Diagnoses of probable AD (n = 31) and probable SIVD (n = 31) were made according to consensus criteria. The diagnosticians were blind to the results of the 10 neuropsychological test scores. RESULTS: There were no significant differences between the groups in age or Mini-Mental State Examination scores. Logistic regression analyses identified 2 neuropsychological tests that best distinguished the groups (sensitivity = 81%; specificity = 84%; positive likelihood ratio = 5.1). These were the recognition memory subtest of the Rey Auditory Verbal Learning Test and the Controlled Oral Word Association Test. The AD group performed better on the oral association test, whereas the SIVD group did better on the recognition memory test. CONCLUSION: Patients with probable AD and probable SIVD can be distinguished with a high degree of accuracy using these 2 neuropsychological tests.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".