Association between White Matter Changes and Cognitive Impairment in Patients with Subcortical Vascular Cognitive Impairment
Bibliographic record
Abstract
Objective To investigate the association between white matter changes of subcortical infarction vascular cognitive impairment and cognitive assess scores.Methods Ninety-one patients with subcortical infarction were consecutively recruited. According to Montreal cognitive assessment(MOCA), they were divided into two groups as subcortical vascular cognitive impairment group(SVCI, 49 cases) and subcortical infarction without cognitive impairment group(SI, 42 cases). The correlation between Cognitive impairment and white matter lesions by analysis the clinical, cognitive impairment and neuroimaging characteristics of patients were explored.Results The incidence of diabetes in SVCI patients is higher than SI patients(38.78% vs 16.67%, P =0.02). Cerebral white matter changes were found in 37 cases(75.51%) of SVCI patients. There is a negative correlation between the degree of white matter lesion and visuospatial executive(Rs =-0.415, P =0.028), memory(Rs =-0.577, P =0.001), attention(Rs =-0.382, P =0.001), delay recall(Rs =-0.389, P =0.041) according to MOCA assessment(Rs =-0.495, P =0.002).Conclusion Diabetes is an important risk factor in SVCI patients. White matter change is the most important imaging features, and refl ects the degree of cognitive impairment.
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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) | 0.000 | 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) | 0.002 | 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".