Lower 25-hydroxyvitamin D is associated with severer white matter hyperintensity and cognitive function in patients with non-disabling ischemic cerebrovascular events
Bibliographic record
Abstract
Objectives This study aimed to investigate the potential correlations among serum 25-hydroxyvitamin D [25(OH)D] levels, white matter hyperintensity (WMH) and cognitive function in patients with non-disabling ischemic cerebrovascular events (NICE). Methods This was a prospective investigation of 160 NICE patients with age of 40 years or older. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA). White matter lesions were evaluated by WMH using Fazekas scores. Spearman correlation analysis and linear regression models were used to identify the associations between serum 25(OH)D levels and cognitive function. Binary logistic regression analysis models were used to evaluate the predictable value of serum 25(OH)D levels and WMH for cognitive impairment. Results Patients with inadequate 25(OH)D levels had lower MoCA score ( P =0.008), and a higher proportion of severe WMH ( P =0.043). Spearman correlation analysis demonstrated that serum 25(OH)D concentrations were positively associated with MoCA score (r s =0.185, P =0.019) while negatively related to the proportion of severe WMH (sWMH) (r s =-0.166, P =0.036).The association between 25(OH)D concentrations and MoCA score remained significant in linear regression (adjusted β=0.012, 95%CI:0.001-0.203).Adjusted binary logistic regression analysis showed that the odds ratio of cognitive impairment with insufficient 25(OH)D concentration was 5.038 (95%CI:1.154-21.988) compared with the sufficient group and the sWMH (OR=2.728, 95%CI:1.230-6.051) was identified as an independent risk factor for cognitive decline in NICE patients. Conclusion Serum 25(OH)D levels and white matter lesions were independently and significantly associated with cognitive impairment in NICE 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".