Relationship between cerebral white matter lesions and cognitive function disorder in old people with vascular risk factors
Bibliographic record
Abstract
Objective To examine the relationship between cerebral white matter lesions(WML)of different severity and the cognitive impairment in old people with vascular risk factors.Methods According to WML score,195 participants with WML were divided into mild WML group,medium WML group and severe WML group.The control group(n= 70) consisted of healthy old people without WML.All participants underwent neuropsychological tests including Mini Mental State Examination,Montreal Cognitive Assessment,Auditory Verbal Learning Test,Logical Memory Test,Rey-Osterrieth Complex Figure Test,Stroop Colour-Word Test,Trail Making Test(Similarity Test,Animals Category Fluency Test,Digital Span Test,Belling Test and Clock Drawing Test.Results The vascular risk factors increased as WML extent aggravated(P0.05).Mild WML group had apparent decline in the memory score,attention score and part of the performance function score compared with the control group,the difference was statistically significant (P0.05,P0.01).The severe WML group had dramatic decline in all cognitive function scores compared with other groups,the differences were statistically significant.All of the cognitive function scores were inversely correlated with severity of WML(P0.01).Conclusion Vascular risk factors could aggravate WML.Mild WML could impair cognitive function,while severe WML showed extensive cognitive impairment.Degree of cognitive impairment was positively correlated to severity of WML in old people with vascular risk factors.
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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) | 0.001 | 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".