P2‐351: VASCULAR PATHOLOGY IN DISTINCT DEGENERATIVE DISEASES
Bibliographic record
Abstract
Vascular disease pathology is a common comorbidity in multiple degenerative diseases and measured as white matter hyperintensities (WMH) on T2 images. The origin and specific role of WMH in different diseases is incompletely understood. In this work, we compared WMH burden in Alzheimers (AD) and Parkinsons disease (PD). We tested association with cerebral blood flow (CBF) as a marker of vascular insufficiency and mean diffusivity (MD) in white matter as a marker of axonal/myelin rarefaction. We also evaluated the association of WMH burden, CBF, and MD with the administered cognitive battery. 114 (Table 1) individuals underwent FLAIR, pCASL, and DTI MRI. Subjects were recruited at the local ADRC and Udall Center and analyzed separately. Lobar WMH burden was calculated and subdivided into lobar periventricular (PVH) and deep (DH) WMH. We tested the hypothesis that PVH and DH burden were associated with MD and CBF respectively in both diseases. Furthermore, we tested association of all cognitive tests (MoCA, MiNT, CATFLU, CRAFT) that were available for all subjects with hypothesized lobar WMH burden, CBF, and MD. We show that AD subjects have a significantly higher PVH burden than NC especially in the frontal and parietal lobes as well as a higher frontal DH burden (Figure 1). No association was detected between PVH and MD or between DH and CBF. Low CATFLU scores were significantly associated with higher temporo-frontal CBF and temporal MD (Figure 2). Low MoCA scores were associated with high parieto-temporal MD. For PD subjects, no significant difference in WMH burden was observed compared to NC. WMH burden was associated with MD in PD. No association was observed with lobar CBF. Temporal WMH was associated with MoCA, CATFLU and MiNT in PD (Figure 3). All tests were adjusted for age and sex.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".