Cerebrovascular and Neurodegenerative Pathologies in Long-Term Stable Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) is considered by some to be a prodromal phase of a progressive disease (i.e., neurodegeneration) resulting in dementia; however, a substantial portion of individuals (ranging from 5-30%) remain cognitively stable over the long term (sMCI). The etiology of sMCI is unclear but may be linked to cerebrovascular disease (CVD), as evidence from longitudinal studies suggest a significant proportion of individuals with vasculopathy remain stable over time. OBJECTIVE: To quantify the presence of neurodegenerative and vascular pathologies in individuals with long-term (>5-year) sMCI, in a preliminary test of the hypothesis that CVD may be a contributor to non-degenerative cognitive impairment. We expect frequent vasculopathy at autopsy in sMCI relative to neurodegenerative disease, and relative to individuals who convert to dementia. METHODS: In this retrospective study, using data from the National Alzheimer's Coordinating Center, individuals with sMCI (n = 28) were compared to those with MCI who declined over a 5 to 9-year period (dMCI; n = 139) on measures of neurodegenerative pathology (i.e., Aβ plaques, neurofibrillary tangles, TDP-43, and cerebral amyloid angiopathy) and CVD (infarcts, lacunes, microinfarcts, hemorrhages, and microbleeds). RESULTS: Alzheimer's disease pathology (Aβ plaques, neurofibrillary tangles, and cerebral amyloid angiopathy) was significantly higher in the dMCI group than the sMCI group. Microinfarcts were the only vasculopathy associated with group membership; these were more frequent in sMCI. CONCLUSION: The most frequent neuropathology in this sample of long-term sMCI was microinfarcts, tentatively suggesting that silent small vessel disease may characterize non-worsening cognitive 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".