Collagenosis of the Deep Medullary Veins: An Underrecognized Pathologic Correlate of White Matter Hyperintensities and Periventricular Infarction?
Bibliographic record
Abstract
White matter hyperintensities (WMH) are prevalent. Although arteriolar disease has been implicated in their pathogenesis, venous pathology warrants consideration. We investigated relationships of WMH with histologic venous, arteriolar and white matter abnormalities and correlated findings with premortem neuroimaging. Three regions of periventricular white matter were sampled from archived autopsy brains of 24 pathologically confirmed Alzheimer disease (AD) and 18 age-matched nonAD patients. Using trichrome staining, venous collagenosis (VC) of periventricular veins (<150 µm in diameter) was scored for severity of wall thickening and occlusion; percent stenosis by collagenosis of large caliber (>200 µm) veins (laVS) was measured. Correlations were made between WMH in premortem neuroimaging and vascular and white matter pathology. We found greater VC (U(114) = 2092.5, p = 0.005 and U(114) = 2121.5, p = 0.002 for small and medium caliber veins, respectively) and greater laVS (t(110) = 3.46, p = 0.001) in patients with higher WMH scores; WMH scores correlated with VC (rs(114) = 0.27, p = 0.004) and laVS (rs(110) = 0.38, p < 0.001). By multiple linear regression analysis, the strongest predictor of WMH score was laVS (β = 0.338, p < 0.0001). VC was frequent in patients with periventricular infarcts identified on imaging. We conclude that periventricular VC is associated with WMH in both AD and nonAD patients and the potential roles of VC in WMH pathogenesis merit further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".