Relationship of Carotid Intima-media Thickness and Epicardial Fat Thickness with Mild Cognitive Impairment in Elderly Patients with Masked Hypertension
Bibliographic record
Abstract
Background Population aging has become a prominent problem in recent years. At present, there are many studies on hypertension and mild cognitive impairment (MCI) , but few studies on the relationship between masked hypertension (MH) and MCI in elderly patients. Objective To investigate the relationship of carotid intima-media thickness (CIMT) and epicardial fat thickness (EAT) with cognitive dysfunction in elderly patients with MH, to provide a theoretical basis for early detection of mild changes in cognitive function in this group. Methods A total of 255 cases were selected from Municipal Hospital of Traditional Chinese Medicine of Jiayuguan from January 2019 to February 2022, including 173 elderly inpatients and outpatients diagnosed with MH (MH group) , and 82 elderly healthy people with normal blood pressure (control group) . Ambulatory blood pressure monitoring, CIMT and EAT measurement were performed in both groups, and relevant indicators were recorded. The Montreal Cognitive Assessment (MoCA) scale was used to assess the cognitive function. Binary Logistic regression analysis was used to explore the factors associated with MCI in MH. Results Compared with control group, MH group had greater average age, and higher levels of average clinic systolic blood pressure (SBP) , clinic diastolic blood pressure (DBP) , 24 h ambulatory SBP, 24 h ambulatory DBP, 24 h SBP coefficient of variation, 24 h DBP coefficient of variation, CIMT and EAT, as well as less average years of education (P<0.05) . The average scores of executive function/visuospatial ability, animal naming, attention, language, abstraction, delayed recall and average total MoCA score in MH group were significantly lower than those in control group (P<0.05) . Correlation analysis showed that the total score of MoCA was negatively correlated with age, 24 h DBP coefficient of variation, CIMT, and EAT (P<0.001) . Binary Logistic regression analysis indicated that CIMT〔OR=48.282, 95%CI (10.734, 217.168) 〕, EAT〔OR=2.124, 95%CI (1.057, 4.269) 〕 were associated with MCI in MH (P<0.05) . Conclusion Increased age, lower education level, increased 24 h SBP coefficient of variation, and increased CIMT and EAT values are risk factors for cognitive dysfunction in elderly patients with MH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".