Analysis on Risk Factors of Type 2 Diabetes Mellitus with Mild Cognitive Dysfunction
Bibliographic record
Abstract
Objective To investigate the risk factors of mild cognitive impairment (MCI) in patients with type 2 dia betes (T2DM), and the clinical evidence for the early diagnosis and treatment thereof. Methods A total of 217 T2DM pa tients were divided into T2DM with MCI group (n=92) and T2DM with normal cognitive function(NMCI) group (n=125). Mon treal cognitive assessment scale (MoCA) and activities of daily living scale (ADL) were used to assess the functional status in two groups of patients. The general clinical data and biochemical indicators were obtained and compared in two groups. Re sults There were statistical differences in age, smoking history, education status, high sensitive C reactive protein (hs-CRP), coronary heart disease, hypertension, glycated hemoglobin A1c (HbA1c) and T2DM history between two groups. Re sults of univariate logistic regression analysis showed that old age, longer course of T2DM, smoking history, higher hs-CRP and HbA1c, complicated with coronary heart disease and hypertension were risk factors for T2DM with MCI, while the higher education status was a protective factor. Multiple logistic regression analysis showed that old age and longer T2DM history were risk factors, and the higher education was a beneficial factor for T2DM with MCI. Conclusion Many risk factors may play a part in T2DM with MCI. Early detection and prompting medical attention may help prevent and decrease the preva lence of MCI in patients with T2DM.
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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 | 0.000 | 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) | 0.002 | 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".