Study on cognitive dysfunction and analysis of risk factor of patients with type-IIdiabetes mellitus
Bibliographic record
Abstract
To investigate the risk factors of mild cognitive impairment(MCI) in patients with type Ⅱ diabetes(T2 DM),a total of 165 T2 DM patients were divided into T2 DM with MCI group(n = 95) and T2 DM with normal cognitive function(NMCI) group(n = 70). Montreal cognitive assessment scale(MoCA) was used to assess the functional status in two groups of patients. Non condition logstic regression was used to analyze the related factors of cognitive dysfunction. Compared with the control group,the diabetes course,blood levels of HbAIc,fasting insulin,total cholesterol,low-density lipoprotein-cholesterol,homocysteic acid,and micro urine protein significantly increased. There were no significant differences in BMI,fasting blood glucose,postprandial 2 h blood sugar,triglycerides,high density lipoprotein-cholesterol,creatinine,blood uric acid,contractive pressure,and diastolic blood pressure between the two groups. Multiple regression analysis showed that older age,inefficient control of blood glucose,long duration of diabetes mellitus,history of hypertension,diabetic nephropathy,and diabetic perineuropathy were significantly independent determinant for the T2 DM with cognitive dysfunction. Many risk factors may play a part in T2 DM with MCI. Early detection and prompting medical attention may help prevent and decrease the prevalence of MCI in patients with T2 DM.
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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 | 0.001 |
| 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 | 0.001 |
| 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".