Effect of the severity of white matter lesion on cognitive function: a clinical study
Bibliographic record
Abstract
Objective To investigate the effect of the severity of white matter lesion( WML) on cognitive function. Methods A total of 48 patients diagnosed as WML with head magnetic resonance imaging( MRI) at Beijing Electric Power Teaching Hospital,Capital Medical University from January 2012 to January 2013 were enrolled. The cognitive function of the patients was assessed with Montreal cognitive assessment( MoCA) scale and the mini-mental state examination( MMSE) scale. The severity of WML was scored by using age-related white matter changes rating scale( ARWMCRs). According to ARWMCRs score,the WML patients were divided into either a mild WML group( 7 points) or a moderate to severe WML group( ≥ 7 points). The correlation between ARWMCRs scores and cognitive function scores was analyzed. Results The MoCA score in the mild WML group was 22. 3 ± 3. 0. It was higher than 20. 3 ± 2. 3 in the moderate to severe WML group( P 0. 01). There was no significant difference in MMSE scores between the two groups. ② Multivariate stepwise regression analysis showed that ARWMCRs score was negatively correlated with MoCA score( b =- 0. 105,P 0. 01) and MMSE score( b =- 0. 057,P 0. 01). ③ Spearman correlation analysis showed that ARWMCRs score was negatively correlated with the subitems in the MoCA scale,including visuospatial executive function( r =- 0. 398),immediate memory( r =- 0. 459),attention( r =- 0. 332),language( r =- 0. 332),abstraction( r =- 0. 229), delayed memory( r =- 0. 348),and classification reminder( r =- 0. 236)( all P 0. 01); and it was negatively correlated with the subitems in the MMSE scale score,including computing power( r =- 0. 235),delayed memory( r =- 0. 294),and language( r =- 0. 423,all P 0. 01). Conclusions The severity of WML has influence on cognitive function. The more severe the WML,the more severe the cognitive impairment will be.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".