A Pilot Study for Investigating Differences between Alzheimer’s Patients with and without Significant Vascular Pathology
Bibliographic record
Abstract
Distinguishing Alzheimer’s disease (AD) from mixed Alzheimer’s and vascular dementia (VD) is a challenging task. In this study, we explored the differences between AD patients and a group with a mixed pathology of AD with cerebrovascular disease (CVD) by analyzing the volumes of several brain regions vulnerable to AD and evidenced by white matter hyper-intensities (WMHs). Moreover, we investigated the correlation between brain volumes and the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) scores of the AD and AD-CVD groups. We collected T1-weighted Magneti-zation Prepared Acquisition with Gradient Echo (MPRAGE) MRI scans from 9 AD participants and 8 AD-CVD participants. Then, we performed the region of interest (ROI) analysis over the MRI data to measure the gray matter (GM) volume of the hippocampus, frontal gyrus, and precuneus as well as the cerebrospinal fluid (CSF) volume of ventricles. Also, we calculated the volume of white matter hyper-intensities (WMHs) of the whole brain and of the frontal-temporal (FT) area. The results did not show any correlation between the baseline ADAS-Cog scores of AD participants and their volumes of above-affected areas and WMHs, while in the AD-CVD group, the CSF volume in ventricles showed a high correlation with ADAS-Cog scores (Spearman’s ρ = 0.714). We did not observe any statistically significant difference in these volumes between AD patients and AD-CVD group.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".