Characterization of the Hemodynamic Profile of Early Alzheimer's Disease via Arterial Spin Labeling Magnetic Resonance Imaging
Bibliographic record
Abstract
Arterial spin labeling is a completely non-invasive method for blood-flow measurement techniques. Alzheimer's disease pathology includes microvascular abnormalities in addition to practically all risk factors having a vascular component that reduces cerebral perfusion. Hemodynamic parameters of cerebral blood flow and arterial transit time were estimated via single-compartment modeling of pseudo continuous arterial spin labeling data and neurocognitive test scores (Alzheimer's disease assessment scale and mini-mental state examination) were compared between a group of healthy (N=20) and early Alzheimer's disease (N=25) subjects before and six months after the Alzheimer's subjects began treatment with cholinesterase inhibitors. The early Alzheimer's group showed improved CBF after 6 months' treatment in every Alzheimer's-prone region except the medial and lateral temporal lobes. No difference in arterial transit time was found between groups, indicating that the pathophysiological process causing hypoperfusion in Alzheimer's disease may differ from vascular dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".