Expression and clinical significance of 25-hydroxyvitamin D, insulin-like growth factor 1, and beta-2 microglobulin in cognitive dysfunction after ischemic stroke in the elderly
Bibliographic record
Abstract
We aimed to unveil the clinical significance of serum 25-hydroxyvitamin D (25-OH-VD), insulin-like growth factor 1 (IGF-1), and beta-2 microglobulin (β2-MG) levels in cognitive dysfunction after ischemic stroke (IS) in the elderly. A total of 160 geriatric IS patients admitted to our hospital were retrospectively collected. The patients' serum 25-OH-VD, IGF-1, and β2-MG levels were detected, and the correlation between the three levels and the patients' National Institutes of Health Stroke Scale (NIHSS) and Montreal Cognitive Assessment (MoCA) scores was tested by the Pearson test. The diagnostic values of 25-OH-VD, IGF-1, and β2-MG for cognitive dysfunction and risk factors inducing cognitive dysfunction in the elderly after IS were evaluated. NIHSS score was negatively correlated with serum 25-OH-VD and IGF-1 levels, and positively correlated with serum β2-MG levels; MoCA score exhibited an inverse correlation. Diabetes, years of education <12 years, age, and serum high-sensitive C-reactive protein, cystatin C, 25-OH-VD, IGF-1, and β2-MG levels were independent factors for the development of cognitive dysfunction after IS in the elderly. The detection of 25-OH-VD, IGF-1, and β2-MG may be important for assessing the occurrence of cognitive dysfunction and the severity of the disease in patients.
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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) | 0.000 | 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".