Clinical Analysis of the Relationship between Vascular Cognitive Impairment and Infarction Locations and White Matter Lesions at Early Stages of Acute Cerebral Infarction
Bibliographic record
Abstract
Objective To analyze the influences of infarction locations and white matter lesions on vascular cognitive impairment(VCI) of acute cerebral infarction patients at their early stages.Methods Consecutive acute cerebral infarction patients underwent Clinical Dementia Rating(CDR),Mini-Mental State Examination(MMSE) and Montreal Cognitive Assessment(MoCA).Analyses were performed to study the association between cognitive impairment and infarction locations including cortical infarction,subcortical critical site infarction,subcortical non-critical site infarction and white matter lesions including leukoaraiosis and non-leukoaraiosis.Results The mean age of cognitive impaired patients was significantly higher than that of normal cognition patients(67.31±10.88 vs 57.09±9.91,P=0.015).The mean score of National Institute of Health Stroke Scale(NIHSS) of cognitive impaired patients was significantly higher than that of normal cognition patients(3.0[2.0~4.0] vs 1.0[1.0~2.0],P=0.012).The mean score of Barthel index of cognitive impaired patients was significantly lower than that of normal cognition patients(81.67±23.55 vs 95.91±12.00,P=0.029).There was significant difference in infarction locations between two groups of patients(P=0.042).Cortical infarction was more popular in vascular cognitive impaired patients.The mean score of the domain of visuospatial executive function in the cortical stroke patients was significantly lower than that of subcortical non-coritical site stroke ones(1.5[0.0~3.0] vs 3.0[2.0~4.0],P=0.016).White matter lesions have no significant relationship with VCI.Conclusion Age,severe neurological deficits,cortical infarcton and ability of daily life were highly related with cognitive impairment of the acute cerebral infarction patients.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".