Association between Serum 25-Hydroxyvitamin D Level and Cognitive Impairment in Patients with White Matter Lesions: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: We aimed to observe the relationship between serum 25-hydroxyvitamin D (25-[OH] D) and different cognitive domains, and to evaluate the predictive value of 25-(OH) D level for cognitive impairment in patients with white matter lesions (WML). METHODS: The differences in clinical data including 25-(OH) D were analyzed between cognitive normality (n = 87) and impairment (n = 139) groups, and variant cognitive domains were analyzed between groups of different levels of serum 25-(OH) D. Risk factors for cognitive impairments were evaluated with multivariate logistic regression analysis; a receiver operating characteristic (ROC) curve of 25-(OH) D levels was used to examine the association between 25-(OH) D and WML with cognitive dysfunction. RESULTS: As the severity of WML increased, the proportion of patients with a low level of serum 25-(OH) D increased (p < 0.05). The total MoCA (Montreal Cognitive Assessment) scores and all domain scores except naming were significantly lower in patients with low levels of serum 25-(OH) D than in patients with high levels of serum 25-(OH) D (p < 0.05). Multivariate logistic regression analyses showed that serum 25-(OH) D levels were independently correlated with cognitive impairment. In the ROC analysis, the optimal cut-off value for 25-(OH) D was 17.53 with 76% sensitivity and 70% specificity (AUC =0.751, 95% CI: 0.674-0.819, p < 0.05). CONCLUSION: We observed that vitamin D deficiency is associated with multiple areas of cognitive impairment and that it is an independent risk factor for cognitive impairment in WML.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".