Risk Factors of Mild Cognitive Impairment in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Objective To evaluate the risk factors of mild cognitive impairment(MCI) in patients with type 2 diabetes.Methods Montreal Cognitive Assessment(MoCA)(Beijing Version) was used as cognition assessment tool.93 type 2 diabetic patients with MCl(MCI group) and 49 type 2 diabetic patients with normal cognitive function(NC group) were enrolled as subjects.Information of disease history,family history,BMI,WHR,FPG,HbA1c,C-P,blood lipid,SBP,DBP and carotid color ultrasound were collected.Results There were statistically significant differences in the history of hypertension,course of diabetes mellitus,C-P level,Max C-IMT,Min carotid resistant index(C-RI) between the MCI group and NC group(P0.05).No statistically significant differences were found in the history and course of hyperlipidaemia,history of diabetes mellitus,course of HBP,sex composition,BMI,WHR,blood sugar(FPG,HbA1c),SBP,DBP and blood lipid between the MCI group and NC group(P0.05).MoCA scores were positively correlated with C-P level(P0.01),and were negatively correlated with the history of HBP,course of diabetes mellitus,Max C-IMT and Min C-RI(P0.05).Multiple regression analysis showed that history of hypertension,C-P level and Min C-RI were independent risk factors for the MoCA scores(P0.01).Conclusion The course of diabetes mellitus,history of HBP,C-P levels,C-IMT and C-RI might be risk factors for MCI in type 2 diabetic patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".