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 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".