COMPARING A NEUROPATHOLOGICAL INDEX WITH TRADITIONAL PATHOLOGY IN PREDICTING ALZHEIMER’S DEMENTIA
Bibliographic record
Abstract
Background: The hallmark neuropathological features of Alzheimer’s disease (AD) don’t correlate well with clinical dementia, suggesting cognitive impairment may be multifactorial in older adults with AD. We aimed to assess whether an index of diverse neuropathological features was more strongly associated with Alzheimer’s-type dementia than traditional AD neuropathological hallmarks. Methods: This was a cross-sectional analysis of data from the Rush Memory and Aging Project. We constructed a neuropathology index (NPI) using the deficit accumulation approach, as the mean of 10 variables coded between 0 (no pathology) and 1 (severe pathology): percentage of amyloidβ, neurofibrillary tangle density, presence of Lewy bodies, hippocampal sclerosis, cerebral infarcts, cerebral amyloid angiopathy, arteriolosclerosis, atherosclerosis, and TDP-43. A traditional pathology score included plaques (diffuse/neuritic) and tangles. A 41-item frailty index of clinical health data was also calculated for each individual. Cognitive status was determined as AD or no dementia by clinical consensus (all other forms of dementia were excluded). Results: The mean age of 645 included participants was 89.7 ± 6.2 years, 68% female. The NPI ranged from 0–0.87, mean 0.36 ± 0.16. In a logistic regression model controlling for age, sex, and frailty, both NPI and traditional pathology were significantly associated with dementia diagnosis (p<0.001). The NPI outperformed the traditional pathology measure in its ability to classify dementia status (C-statistic 0.80, 95% CI 0.77–0.85 vs. 0.74, 0.70–0.78). Conclusion: An NPI captures information over and above traditional hallmark pathological measures of AD and may help characterize the multifactorial etiologic pathway of dementia in AD.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".