O2‐05‐07: The neuropathological features associated with Alzheimer's disease diagnosis in the oldest old versus the young old
Bibliographic record
Abstract
Most studies that have investigated the neuropathological features of Alzheimer's disease (AD) have examined the young-old (65-85 years of age). However, the oldest old are the fastest growing demographic and have especially high risk for developing AD. The few studies of the neuropathological features of dementia in the oldest old have been small and have focused on few neuropathological features. The objective of this study was to examine whether the association between neuropathological features and clinical AD diagnosis varies by age (young-old: 70-79 years, oldest old: ≥90 years). We examined 5021 people (age ≥ 70 at death) from the National Alzheimer's Coordinating Center database who had a clinical diagnosis of normal cognition (within 1 year prior to death) or AD and a neuropathological examination post mortem. We analyzed the association between neurofibrillary tangles (NFT), neuritic plaques (NP), diffuse plaques, amyloid angiopathy, Lewy Bodies (LB), large infarcts, atherosclerosis, and lacunes and diagnosis of AD using logistic regressions. We used an interaction to examine the effect of age and receiver operating characteristic (ROC) analysis to evaluate the predictive value of the model. Of the participants, 56% were female, 18% were ≥90 years, and 82% had a diagnosis of AD. All neuropathological features except large infarcts and lacunes were positively associated with AD. The relationship between most neuropathological features and AD was attenuated in the oldest old compared to the young-old. Correspondingly, the predictive value of the model with all neuropathic features included was worse in the oldest old (ROC area under the curve, 95% confidence interval: 0.83, 0.79-0.87) compared to young old (0.92, 0.89-0.96). Neuropathological features appear to be less predictive of AD in the oldest old compared to the young old. The explanations for this difference are not clear but may be attributed to survival bias, different biology, or genetic factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".