Total Cerebral Small Vessel Disease Burden on MRI Correlates With Cognitive Impairment in Outpatients With Amnestic Disorders
Bibliographic record
Abstract
Objectives: The main markers of cerebral small vessel disease (cSVD) on MRI may be entered into a scoring system, with the total score representing the overall burden of cSVD. An association between total cSVD score and cognitive dysfunction has been reported in several cohorts. The present study aimed to investigate this association in outpatients with amnestic disorders. Materials and Methods: Outpatients with amnestic complaints in a memory clinic (n = 289) were recruited retrospectively. All the patients had undergone clinical and cognitive evaluation at first presentation. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) scale. The total cSVD score was based on the following markers on MRI: lacune; white matter hyperintensities, microbleed, and enlarged perivascular spaces. The association between total cSVD score and MoCA score was tested via Spearman's analysis and a linear regression model. Results: Among the 289 patients, rates for 0–4 cSVD markers respectively ranged from 30.4 to 2.8%. A multiple linear regression model revealed an inverse correlation between the total cSVD score and MoCA score. The association remained significant after adjusting for gender, age, education, levels of medial temporal lobe atrophy, and classical vascular risk factors [β = −0.729, 95% CI (−1.244, −0.213); P = 0.006]. When individual markers were individually analyzed after adjusting for the same factors, only microbleed associated with MoCA score [β = −3.007, 95% CI (−4.533, −1.480), P < 0.001]. Conclusions: A significant association was demonstrated between total cSVD score and cognitive performance in the outpatients with amnestic disorders.
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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 | 0.003 |
| 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".