Delayed primacy recall performance predicts post mortem Alzheimers disease pathology from unimpaired ante mortem cognitive baseline
Bibliographic record
Abstract
INTRODUCTION: We propose a novel method to assess delayed primacy in the CERAD memory test. We then examine whether this measure predicts post mortem Alzheimers disease (AD) neuropathology in individuals who were clinically unimpaired at baseline. METHODS: A total of 1096 individuals were selected from the Rush Alzheimers Disease Center database registry. All participants were clinically unimpaired at baseline, and had subsequently undergone brain autopsy. Average age at baseline was 78.8 (6.92). A Bayesian regression analysis was carried out with global pathology as outcome; demographic, clinical and APOE data as covariates; and cognitive predictors, including delayed primacy. RESULTS: Global AD pathology was best predicted by delayed primacy. Secondary analyses showed that delayed primacy was mostly associated with neuritic plaques, whereas total delayed recall was associated with neurofibrillary tangles. DISCUSSION: We conclude that CERAD-derived delayed primacy is a useful metric for early detection and diagnosis of AD in unimpaired individuals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".