Pathologies Underlying Longitudinal Cognitive Decline in Old Age (P6.224)
Bibliographic record
Abstract
Objective: This study examines the association between trajectories of memory and executive function decline and brain pathologies. Background: Contributions of different brain pathologies to domain-specific cognitive trajectories is not well studied. Methods: Two-hundred-twenty-seven Oregon Alzheimers Disease Center research participants who were cognitively intact at entry were followed on average for 7.3 years with annual neuropsychological testing until death and autopsy. Last evaluation was on average 8.4 months prior to death. Mean age at death was 93.8 years. Mixed-effects models examined the relationship between longitudinal decline in memory and executive function and pathology (neurofibrillary tangles (NFTs), neuritic plaques (NPs), gross infarcts, hippocampal sclerosis, and, Lewy bodies) and APOE genotype, adjusting for duration of follow up, age at death and years of education. In secondary models diagnosis was added as a time varying covariate to the main models. Results: Memory decline over time was associated with presence of hippocampal sclerosis (p=0.01), Braak score 5 or 6 (p=0.003) and the e4 allele (p=0.007). Executive function decline over time was associated with presence of gross infarcts (p=0.005), and hippocampal sclerosis (p=0.05). Diagnosis of MCI or dementia, as a time-varying covariate, was associated with steeper declines in memory and executive function independent of pathological measures (p<. 0001). Conclusions: Memory decline was most strongly associated with NFT burden, and executive function decline with cerebrovascular disease. Presence of the e4 allele was associated with memory decline independent of pathologies, suggesting that APOE may be involved in mechanisms beyond those related to amyloid metabolism.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".