[Correlation analysis of lipid level and cognitive function in middle-aged and elderly diabetic population from 2 communities in Beijing in 2017].
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between serum lipid level and cognitive function in middle-aged and elderly patients with type 2 diabetes mellitus(T2 DM). METHODS: A cross-sectional study was implemented in 2017 to explore the correlation between lipid level and cognitive function in middle-aged and elderly diabetic patients. A random sample of 1526 middle-aged and elderly people were recruited from 2 communities in Beijing. Fasting blood samples were collected for blood lipid level detection, and the cognitive function of the subjects was assessed by the Montreal Cognitive Assessment Scale(MoCA). Logistic regression model was used to analyze the correlation between serum lipid levels and mild cognitive impairment(MCI) in elderly and T2 DM patients and control group. RESULTS: Compared with the control group, there were significant differences in blood lipid levels in T2 DM group. The levels of total cholesterol(TC) and high density lipoprotein cholesterol(HDL-C) in T2 DM group were lower than those in the control group, while triglyceride(TG) and low density lipoprotein cholesterol(LDL-C) were higher than those in the control group. Logistic regression analysis showed that high level of TC was a risk factor for MCI in patients with T2 DM, while high level of LDL-C was a protective factor, but no association was observed in the control population. CONCLUSION: Phenotype of T2 DM may affect the relationship between lipid level and cognitive function in middle-aged and elderly people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".