Study on the Correlation Between Serum Macrophage Migration Inhibitory Factor and Cognitive Impairment after Stroke and Its Relationship with Serum Inflammatory Factors
Bibliographic record
Abstract
Objective: To analyze the correlation between serum macrophage migration inhibitory factor (MIF) and cognitive impairment after stroke, as well as its relationship with serum inflammatory factors. Methods: Stroke patients in our hospital from September 2022 to June 2023 were included as the research subjects. They were divided into the control group (without cognitive impairment, n = 43) and the observation group (with cognitive impairment, n = 51) based on whether cognitive impairment occurred. The levels of serum factors and cognitive functions of the two groups were compared, the multiple factors influencing cognitive impairment after stroke were analyzed, and the correlation between the level of serum macrophage MIF and cognitive impairment or inflammatory factors was explored. Results: The levels of serum tumor necrosis factor -α (TNF-α), interleukin-6 (IL-6), and MIF were lower, while the scores of the Montreal Cognitive Scale (MoCa) and the Mini-Mental State Examination (MMSE) were higher in the control group than in the observation group (p < 0.05). Combined diabetes, hypertension, hyperlipidemia, TNF-α, IL-6, and MIF were risk factors affecting cognitive impairment after stroke (p < 0.05). MIF was negatively correlated with both MoCA and MMSE scores (p < 0.05), and positively correlated with the levels of TNF-α and IL-6 (p < 0.05). Conclusion: The levels of serum TNF-α, IL-6 and MIF have a certain correlation with cognitive impairment in stroke patients, the abnormal increase of which is a risk factor affecting the occurrence of cognitive impairment in stroke patients. Early detection of serum levels of TNF-α, IL-6 and MIF in patients can effectively prevent the occurrence of cognitive impairment and improve the prognosis of 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 | 0.001 | 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 | 0.000 | 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".