P2‐301: Inter‐test variability contributes independently to the five‐year prediction of Alzheimer's disease in nondemented older adults
Bibliographic record
Abstract
Our purpose was to examine the value of inter-test variability for predicting progression to a diagnosis of probable Alzheimer's disease (AD) in initially nondemented participants of the Canadian Study of Health and Aging (CSHA). The CSHA included 3 waves: CSHA-1 (1991 to 1992), CSHA-2 (1996 to 1997), and CSHA-3 (2001 to 2002). All participants who underwent neuropsychological assessment at CSHA-2 and received a diagnostic assessment at CSHA-3 were eligible for inclusion in this analysis. Dispersion was characterized as the intra-individual standard deviation across standardized scores on three tests: RAVLT delayed recall, animal fluency and WMS Information. These tests were selected because they were previously shown to be the strongest neuropsychological predictors of AD in the CSHA battery. Participants were classified based on their CSHA-3 diagnostic outcome (probable AD vs. all other diagnoses) and the predictive accuracy of the dispersion variable was examined by including it in a logistic regression analysis including the three test scores, age, and education. 505 CSHA-2 participants were eligible for inclusion in this analysis. 40 participants were subsequently diagnosed with probable AD at CSHA-3, and the remaining 455 remained stable or progressed to other diagnostic outcomes. The logistic regression model including age, education, the three neuropsychological test scores and the dispersion variable was significant, X2(2) = 6.38, P = 0.04. Within this model dispersion was a significant independent predictor of probable AD (P = 0.01). In this epidemiologic sample of nondemented older adults, a measure of inter-test variability uniquely contributed to the prediction of probable Alzheimer's disease. This result replicates and extends previous findings implicating the potential importance of variability for identifying those at risk of cognitive impairment.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".