Cognitive features of white matter lesions accompanied by different risk factors of cerebrovascular diseases
Bibliographic record
Abstract
BACKGROUND: The relationship between different risk factors and the cognitive impairment of white matter lesions (WML) remains poorly understood. OBJECTIVES: To investigate the features of cognitive impairment of patients diagnosed with WML accompanied by different risk factors of cerebrovascular diseases. MATERIAL AND METHODS: A total of 157 cases of WML patients were divided into no risk factor group (n = 26), hypertension group (n = 35), diabetes mellitus group (n = 27), dyslipidemia group (n = 30), and mixed factors group (n = 39). RESULTS: The severity of WML (Fazekas score) in the hypertension and mixed factors groups was higher than in the non-risk factors group. The Montreal Cognitive Assessment (MoCA) scores in the hypertension and mixed factors groups were lower than in the non-risk factors group. The scores of MoCA, immediate memory and delayed recall in the hypertension and mixed factors groups with Fazekas score ≥3 were lower than in the peer group with Fazekas score <3. The scores of MoCA and immediate memory in the hypertension and mixed factors groups with Fazekas score ≥3 were lower than in the non-risk factors group with Fazekas score ≥3. CONCLUSIONS: Hypertension aggravates the severity of WML and cognitive impairment. The severity of WML is positively correlated with the severity of cognitive impairment accompanied by these risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".