Correlation Analysis Between Local Cerebral Blood Flow and Severity of Vascular Cognitive Dysfunction
Bibliographic record
Abstract
Research methods: This paper analyses the correlation between cerebral blood flow perfusion caused by cerebral vascular stenosis and the reduction of patients with cognitive dysfunction and white matter damage. A total of 118 patients with reduced cerebral blood flow perfusion due to cerebrovascular stenosis were selected to be included in the disease group, and 118 patients with no cerebrovascular stenosis and no neurological disease were included in the control group. The cerebral blood flow perfusion index and cognitive function index were compared between the two groups of patients. The correlation between each index and the degree of brain white matter damage was analysed. Results: The scores of brain white matter damage in patients with disease group were higher than those in control group, and cCBV, cCBF, TTP, MTT, MoCA, MMSE, ADL, and WMS were lower than those in control group, and the difference was statistically significant (P < 0.05). cCBV, cCBF, TTP, MTT, and white matter damage scores were highly correlated with MoCA, MMSE, ADL, and WMS (P < 0.05). There is a clear correlation between cerebral vascular perfusion, cognitive dysfunction, and white matter damage in patients with cerebrovascular stenosis. The more severe the perfusion of cerebral blood flow, the more severe the cognitive dysfunction and the white matter damage.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".