Relationship between cerebral small vessel disease assessed by magnetic resonance imaging and cognitive impairment
Bibliographic record
Abstract
Objective:To investigate the relationship between cerebral small vessel disease(CSVD) assessed by magnetic resonance imaging and cognitive impairment.Methods:A total of 93 patients with CSVD,characterized by white matter lesions(WML) and lacunar infarction( LI) were divided into cognitive impairment( CI) and no cognitive impairment(NCI) groups according to Montreal Cognitive Assessment(MoCA).The severity of WML was assessed,and the total number of LI and the numbers of LI in different regions of the brain were recorded.Their correlation with CI was analyzed.Results:As compared with the NCI group,the proportion of alcohol,hypertension and blood TC and LDL-C concentration in CI group were significantly increased( P 0.05);there were no significant differences in the proportion of smokers and diabetes mellitus between two groups(P 0.05).The total number of LI and the scores of WML were significantly increased in CI group than those in NCI group(P 0.01).After controlling the impact of age,sex and the scores of WML,the total number of LI could account for 55.1% of the scores of MoCA.LI numbers in frontal,temporal and basal ganglia area were positively correlated to MoCA scores(P 0.01),while those in parieto-occipital and infratentorial area were not correlated significantly(P 0.05).Conclusion:The WML and the numbers and locations of LI are related to CI symptoms.The numbers of LI at frontal,temperal and basal ganglia areas were independent prediction of CI in patients with CSVD.
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) | 0.000 | 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".