Histopathological Analysis of Cerebrovascular Lesions Associated With Aging
Bibliographic record
Abstract
Cerebrovascular disease (CVD) has been associated with cognitive impairment. Yet, our understanding of vascular contribution to cognitive decline has been limited by heterogeneity of definitions and assessment, as well as its occurrence in cognitively healthy aging. Therefore, we aimed to establish the natural progression of CVD associated with aging. We conducted a retrospective observational study of 63 cognitively healthy participants aged 19-84 years selected through the histological archives of the CHU de Québec. Assessment of CVD lesions was performed independently by 3 observers blinded to clinical data using the Vascular Cognitive Impairment Neuropathology Guidelines (VCING). We found moderate to almost perfect interobserver agreement for most regional CVD scores. Atherosclerosis (ρ = 0.758) and arteriolosclerosis (ρ = 0.708) showed the greatest significant association with age, followed by perivascular hemosiderin deposits (ρ = 0.432) and cerebral amyloid angiopathy (CAA; ρ = 0.392). Amyloid and tau pathologies were both associated with higher CVD load, but only CAA remained significantly associated with amyloid plaques after controlling for age. Altogether, these findings support the presence of multiple CVD lesions in the brains of cognitively healthy adults, the burden of which increases with age and can be quantified in a reproducible manner using standardized histological scales such as the VCING.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".