Correlation analysis between the LDL-C in serum and the onset of transient ischemic attack caused by CSVD
Bibliographic record
Abstract
The aim of this study was to investigate the correlation between the low-density lipoprotein cholesterol (LDL-C) in serum and the onset of transient ischemic attack caused by cerebral small vascular disease (CSVD). Between September 2012 and September 2015, 249 patients who were diagnosed as CSVD were randomly enrolled in this study. According to MRI results, patients were divided into the patient and control groups. In the patient group, the patients were further subdivided into the white matter lesion (WML) group (n=86) and lacunar infarction (LI) group (n=53). Head MRI and/or CT were conducted on all the patients. This included T1 and T2 phases, diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR). Additionally, mini-mental status examination (MMSE) test and Montreal cognitive assessment (MoCA) test were carried out on all the patients. As a result, the age, total cholesterol (TC) level and low-density lipoprotein (LDL) levels in the patient group were higher than those in the control group (p<0.05). The MMSE and MoCA scores in the patient group were significantly lower than those in the control group (p<0.05). With all the risk factors being set as independent variables and small vessel disease (SVD) as the dependent variable, we performed the logistic regression analysis and correlation analysis for paired data, and found that the increase in LDL was correlated to the onset of SVD, OR=1,321. After adjustment of other risk factors, we enrolled the level of triglyceride (TG) into the multivariable analysis and obtained a statistically significant difference (p<0.05). In conclusion, LDL is a major risk factor affecting the onset of transient ischemic attack (TIA) induced by CSVD. Patients with hyperlipidemia should receive head MRI or CT examination to eliminate the probability of the existence of CSVD. To reduce the occurrence of adverse events in clinical practice, we can perform early intervention in SVD by decreasing the level of LDL, improving the endothelial function of small vessels and applying the anti-inflammation and nerve-protection methods.
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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 | 0.001 |
| 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".