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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".