轻度血管性认知障碍影响因素分析 Influencing Factors of Mild Vascular Cognitive Impairment
Bibliographic record
Abstract
目的 探讨轻度血管性认知障碍（mild vascular cognitive impairment，mVCI）的影响因素。 方法 选取2015年8月-2018年10月收治于邯郸市第一医院神经内科的发病14 d内急性缺血性卒中 的患者为研究对象，行简易精神状态检查表、蒙特利尔认知评估量表测评，对其认知功能进行评估， 并根据其得分情况分为mVCI组及认知正常组。对比两组患者的临床资料的差异，采用单因素分析和 多因素Logistic回归分析探讨mVCI的影响因素。 结果 最终共纳入205例患者，mVCI组97例,认知正常组108例。①单因素分析显示，mVCI组患者低 文化程度（P =0.006）、高血压（P =0.032）、糖尿病（P =0.041）及吸烟（P =0.026）的比例显著高于 认知功能正常组，差异有统计学意义。mVCI组患者的血浆同型半胱氨酸水平（P =0.016）、TG水平 （P =0.040）、TC水平（P =0.026）以及匹兹堡睡眠质量指数（P＜0.001）显著高于认知正常组，差异有 统计学意义。②多因素Logistic回归分析显示，同型半胱氨酸水平升高（OR 1.139，95%CI 1.012～1.283， P =0.031）和主观睡眠质量差（OR 1.301，95%CI 1.107～1.530，P =0.001）是mVCI发生的独立危险因素， 而文化程度高（OR 0.652，95%CI 0.434～0.978，P =0.039）是mVCI 的保护性因素。 结论 高同型半胱氨酸血症及睡眠质量差是mVCI的独立危险因素，而文化程度高是mVCI的保护性因素。 Abstract: Objective To explore the influencing factors of mild vascular cognitive impairment (mVCI). Methods Patients with acute ischemic stroke within 14 days from onset who were admitted to Handan First Hospital From August 2015 to October 2018 were selected. All the patients were assessed by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA). According to the scores, these patients were divided into mVCI group and normal cognition (NCI) group. The clinical data were compared between the two groups, using univariate analysis and multivariate logistic regression analysis were used to analyze the influencing factors of mVCI. Results Finally, a total of 205 patients were included in this study, with 97 cases in mVCI group and 108 cases in NCI group. The proportion of patients with low education (P =0.006), hypertension (P =0.032), diabetes (P =0.041) and smoking (P =0.026) in mVCI group were significantly higher than that in NCI group. The levels of plasma homocysteine (P =0.016), triglyceride (P =0.040), cholesterol (P =0.026) and Pittsburgh sleep quality index (P <0.001) were significantly higher in mVCI group than that in NCI group. Elevated plasma homocysteine level (OR 1.139, 95%CI 1.012- 1.283, P =0.031) and multivariate logistic regression analysis showed that poor sleep quality (OR 1.301, 95%CI 1.107-1.530, P =0.001) were independent risk factors for mVCI, and high education degree (OR 0.652, 95%CI 0.434-0.978, P =0.039) was a protective factor. Conclusions Hyperhomocysteinemia and poor sleep quality are independent risk factors for mVCI, while high education degree is a protective factor for mVCI.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".