Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years
Bibliographic record
Abstract
OBJECTIVE: To determine whether neuropsychological tests accurately predict incident Alzheimer disease (AD) after 5 and 10 years in participants of the Canadian Study of Health and Aging (CSHA) who were initially nondemented. METHODS: The CSHA was conducted in three waves: CSHA-1 (1991 to 1992), CSHA-2 (1996 to 1997), and CSHA-3 (2001 to 2002). The 10-year prediction study included those who completed neuropsychological testing at CSHA-1 and received a diagnostic assessment at CSHA-3 (n = 263). The 5-year prediction study included those who completed neuropsychological testing at CSHA-2 and received a diagnostic assessment at CSHA-3 (n = 551). The diagnostic workup for dementia at CSHA-3 was formulated without knowledge of neuropsychological test performance at CSHA-1 or CSHA-2. The authors excluded cases with a baseline diagnosis of dementia or a prior history of any condition likely to affect the brain. Age and education were included in all analyses as covariates. RESULTS: In the 10-year follow-up study, only one test (short delayed verbal recall) emerged from the forward regression analyses. The model with this test and two covariates was significant, chi2 (3) = 31.61, p < 0.0001 (sensitivity = 73%, specificity = 70%). In the 5-year follow-up study, three tests (short delayed verbal recall, animal fluency, and information) emerged from the forward logistic regression analyses. The model was significant, chi2 (5) = 91.34, p < 0.0001 (sensitivity = 74%, specificity = 83%). Both models were supported with bootstrapping estimates. CONCLUSIONS: In a large epidemiologic sample of nondemented participants, neuropsychological tests accurately predicted conversion to Alzheimer disease after 5 and 10 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".