Differential prediction of vascular dementia and Alzheimer's disease in nondemented older adults within 5 years of initial testing
Bibliographic record
Abstract
OBJECTIVE: To determine whether neuropsychological tests and the Hachinski Ischemic Score (HIS) can differentiate incident vascular dementia (VaD) from Alzheimer's disease (AD) in nondemented older adults within 5 years of initial testing. METHODS: The Canadian Study of Health and Aging (CSHA) included three waves: CSHA-1 (1991-1992), CSHA-2 (1996-1997), and CSHA-3 (2001-2002). This analysis included participants of the CSHA who (a) underwent neuropsychological testing and clinical assessment at CSHA-2 and were determined to be nondemented, and (b) underwent diagnostic assessment at CSHA-3. The outcome measure was CSHA-3 diagnosis, classified as VaD (n = 22), probable or possible AD (n = 65), and all other diagnostic outcomes (n = 417). CSHA-3 diagnosticians were blinded to CSHA-2 test scores and diagnoses. Multinomial logistic regression with forward selection was used to determine the ability of the HIS and 15 CSHA-2 neuropsychological tests to predict CSHA-3 diagnostic outcome. The analysis was repeated after removing 15 AD cases with coexisting vascular disease. RESULTS: The HIS and four neuropsychological tests were significant predictors of CSHA-3 diagnostic outcome (χ2 (14) = 149.59, P < .001, R2 = 0.38). Relative to developing VaD, higher HIS (odds ratio [OR]: 0.70; 95% confidence interval [CI]: 0.57-0.86) and Rey Auditory Verbal Learning Test immediate verbal recall scores (OR: 0.77; 95% CI: 0.62-0.97) were associated with lowered odds of developing AD, whereas higher phonemic fluency scores (OR: 1.21; 95% CI: 1.02-1.17) were associated with increased odds of developing AD. Removing AD cases with vascular disease did not affect results. CONCLUSIONS: In an epidemiological sample of nondemented participants, the HIS and two neuropsychological tests contributed to the differential prediction of VaD and AD within 5 years of initial measurement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".