The Relevance of Serum Macrophage Migration Inhibitory Factor Level and Executive Function in Patients with White Matter Hyperintensity in Cerebral Small Vessel Disease
Bibliographic record
Abstract
(1) Objective: To investigate the relationship between serum macrophage migration inhibitory factor (MIF) level and white matter hyperintensity (WMH) and executive function (EF) in cerebral small vascular disease (CSVD), and assess the impact and predictive value of MIF level and Fazekas scores in CSVD-related cognitive impairment (CI) (CSVD-CI); (2) Methods: A total of 117 patients with WMH admitted to the First Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2022 were enrolled. According to the Montreal cognitive assessment (MoCA) scale, subjects were divided into a normal cognitive group and an impaired group. All subjects required serum MIF level, 3.0 T MRI, and neuropsychological evaluation to investigate the risk factors for CDVD-CI, analyze the correlation between MIF level, WMH, and EF, and to analyze the diagnostic value of MIF and WMH degree in predicting CSVD-CI; (3) Results: 1. Fazekas score and MIF level were the risk factors of CSVD-CI. 2. The Fazekas score was negatively correlated with MoCA score, positively correlated with Stroop C-Time, Stroop C-Mistake, Stroop interference effects (SIE)-Time, SIE-Mistake, and color trails test (CTT) interference effects (CIE) (B-A). 3. The MIF level was positively correlated with Fazekas score, Stroop C-Time, SIE-Time, CTT B-Time, and CIE (B-A), and negatively correlated with MoCA score. 4. Fazekas score and MIF level were significant factors for diagnosing CSVD-CI; (4) Conclusion: The Fazekas score and MIF level may be the risk factors of CSVD-CI, and they are closely correlated to CI, especially the EF, and they have diagnostic value for CSVD-CI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".