Inverse Relationship Between Cerebrovascular Lesions and Severity of Lewy Body Pathology in Patients With Lewy Body Diseases
Bibliographic record
Abstract
Cerebrovascular pathology is a major cause of stroke and mortality. Studies on prevalence of cerebrovascular pathologies in dementia with Lewy bodies (DLBs) and Parkinson disease (PD) patients are scarce and contradictory. We aimed to determine the prevalence and severity of cerebrovascular pathologies in DLB and PD and to analyze their relationship to LB pathology. The prevalence and severity of atherosclerosis in the circle of Willis, cerebral amyloid angiopathy, cerebral infarcts, hemorrhages, small-vessel disease, white matter lesions, including the Consortium to Establish a Registry for Alzheimer Disease (CERAD) protocol as well as Braak neurofibrillary tangle stages for AD pathology were analyzed in autopsy-verified DLB (n = 13), PD (n = 102), and control subjects (n = 53). In all patient groups, the extent of LB pathology was inversely correlated to the severity of most vascular pathologies (atherosclerosis, infarcts, and small-vessel disease; all p < 0.05). By contrast, cerebral amyloid angiopathy, CERAD, and Braak neurofibrillary tangle stages were positively correlated with LB pathology (p < 0.05). Whereas the overall prevalence and severity of small-vessel disease, infarcts, hemorrhages, and white matter lesions were comparable among both disease groups, the extents of atherosclerosis, cerebral amyloid angiopathy, CERAD, and Braak neurofibrillary tangle stages were significantly higher in DLB than in those of PD patients (p < 0.05). Microinfarcts were statistically more prevalent in each patient group than in controls, whereas gross infarcts predominated in controls (p < 0.05 each). In conclusion, DLB and PD patients with advanced LB pathology were less likely to show severe cerebrovascular disease or history of stroke.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".