Does cerebral large-artery disease contribute to cognitive impairment?
Bibliographic record
Abstract
Although many patients with cerebral large-artery disease (CLAD) show impaired cognitive performance, the risk factors remain unclear in this population. The objective of this study was to evaluate cognitive impairment and its risk factors in patients with CLAD. We recruited non-demented patients with CLAD from our hospital. CLAD was defined as occlusion or stenosis of over 50% in the carotid artery or middle cerebral artery. We collected patients' biographical data and vascular lesion and imaging data, including periventricular hyperintensity (PVH) and cerebral perfusion. The patients were divided into two groups: cognitive impairment-plus (CoI +) and normal (CoI −) groups, according to their Montreal Cognitive Assessment (MoCA) scores, with a cut-off value of 26. The factors associated with cognitive impairment were examined. Of the 176 patients with CLAD (mean age 70.2 ± 8.3, 40 female), 136 (77.2%) were classified as cognitively impaired. Multivariate analysis indicated that the CoI + group was associated with older age (odds ratio (OR): 1.09, P = 0.011), drinking habit (OR: 7.15, P = 0.003), increased PVH (OR: 3.46, P = 0.003), and decreased cerebral perfusion (OR: 0.897, P = 0.007). Analyses of the MoCA subscores indicated that attention, memory, and orientation were impaired in the CoI + group. Impaired cognition was observed in some of the non-demented patients with CLAD. Older age, drinking habit, severe PVH and decreased cerebral perfusion contributed to their poor cognitive performance. Strict treatment of atherosclerosis and intervention for CLAD might be necessary to prevent cognitive decline in these patients.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".