Change of MR diffusion tensor imaging and its correlation with cognitive impairment in patients with cerebral small vessel disease
Bibliographic record
Abstract
Objective To explore the change of MR diffusion tensor imaging( DTI) and its correlation with cognitive impairment in patients with cerebral small vessel disease( SVD). Methods Neuropsychological scales were conducted in 55 SVD patients to evaluate their cognitive functional status. All the 55 cases underwent conventional MRI and DTI,the fractional anisotropy( FA) and average diffusion coefficient( ADC) values were measured in the regions of interest. Pearson correlation analysis was used to determine the correlation between the FA value of DTI and Montreal Cognitive Assessmen( MoCA) scale score. Results Of the enrolled patients,25 cases were with normal cognitive function( NCI) and 30 cases were with cognitive impairment without dementia( VCIND). Compared with the NCI group,The VCIND patients had lowered FA values in the bilateral centrum semiovale,thalamus,frontal lobes and the left caudate nucleus( P 0. 05- 0. 001); the FA values of bilateral lentiform nucleus and right caudate nucleus had no significant difference; The ADC value was significantly increased in the left caudate nucleus( P 0. 05),the ADC values of other parts had no significant difference. Pearson correlation analysis showed that the FA values of bilateral centrum semiovale,frontal lobe and left caudate nucleus were positively correlated with MoCA scale scores( r =0.279-0.375,P 0.05-0.005). Conclusion The SVD patients with cognitive impairment have a significant decrease in their FA values of frontal lobe and white matter which are closely related to 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 | 0.002 |
| 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) | 0.001 | 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".