Clinical meaningfulness of subtle cognitive decline on longitudinal testing in preclinical AD
Bibliographic record
Abstract
INTRODUCTION: Demonstrating the "clinical meaningfulness" of slowing early cognitive decline in clinically normal (CN) older adults with elevated amyloid-β (Aβ+) is critical for Alzheimer's disease secondary prevention trials and for understanding early cognitive progression. METHODS: Cox regression analyses were used to determine whether 3-year slopes on the preclinical Alzheimer's cognitive composite predicted MCI diagnosis and global Clinical Dementia Rating>0 in 267 Aβ+ CN individuals participating in the Harvard Aging Brain Study, Australian Imaging, Biomarker and Lifestyle Study, and Alzheimer's Disease Neuroimaging Initiative. RESULTS: Steeper preclinical Alzheimer's cognitive composite decline over 3 years was associated with increased risk for MCI diagnosis and global Clinical Dementia Rating>0 in the following years across all cohorts. Hazard ratios using meta-analytic estimates were 5.47 (95% CI: 3.25-9.18) for MCI diagnosis and 4.49 (95% CI: 2.84-7.09) for Clinical Dementia Rating>0 in those with subtle decline (>-.14 to -.26 preclinical Alzheimer's cognitive composite standard deviations/year) on longitudinal cognitive testing. DISCUSSION: Early "subtle cognitive decline" among Aβ+ CN on a sensitive cognitive composite demonstrably increases risk for imminent clinical disease progression and functional 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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".