Study of cognitive impairment in patients with subcortical ischemic vascular disease
Bibliographic record
Abstract
Objective To analyze the cognitive profile of patients with subcortical vascular cognitive impairment by using a set of cognitive measures.Methods Extensive neuropsychological tests including MMSE and covering 5 cognitive domains were performed on 53 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and 25 normal elderly controls(NC) matched in age and gender.The patients were divided into VaD(n=27)and VCIND group(n=26).Results ① The overall level of cognitive performance in these tests was significant inferior in VaD subjects as compared to NC subjects(P0.05).②The patients with VCIND were worse than the normal elders in the tests including MMSE,digit span test(DST) backwards and trail making test(TMT)(P0.05).③Between VaD and VCIND patients,significant differences were found in many fields.Compared with VCIND patients,VaD subjects showed decline on the word recall,TMT,clock drawing test and DST(P0.05).Conclusion ① SIVD is related to comprehensive cognitive impairment,specifically contributed to the deterioration of executive and attention function.The impairments of language and memory are affected slightly.② The predominant impairments in patients with VCIND are executive and attention deficits,while in patients with VaD suffered from the progressive cognitive impairment and overall cognitive decline.
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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 | 0.001 | 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".