Lacunar infarction aggravates the cognitive deficit in the elderly with white matter lesion
Bibliographic record
Abstract
Cerebral white matter lesion (WML) and lacunar infarction (LI) were primary causes of cognitive deficit. Our study aimed to investigate the correlation between LI and cognitive deficit in the elderly with WML. A total of 118 participants (96 WML patients and 22 controls) were consecutively enrolled according to neuroimaging diagnosis of magnetic resonance imaging for this retrospective study. Neuroimaging evaluation and cognitive function assessment were analyzed. Compared with the controls, moderate and severe WML groups had significantly lower scores of Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MOCA). Most cognitive domains of MOCA scores decreased, corresponding to the severity of WMLs. While there was no significant difference in score of MMSE between deep WML (DWML) and periventricular WML (PVL) groups, the scores of visuospatial/executive and naming function domains of MOCA appeared to be low in the DWML group. The scores of MMSE and MOCA were higher in only WMLs (WML-) group than WMLs combined with LIs (WML+) group, except for the naming cognitive domain. Moreover, LIs were independently correlated with the cognitive deficit in the elderly with WMLs. In the elderly with WMLs, the presence of LIs is associated with further aggravation of cognitive deficit.
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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 | 0.000 |
| 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".