A clinical research of risk factors in type 2 diabetic patients with cognitive impairment
Bibliographic record
Abstract
Objective To study the risk factors in type 2 diabetic patients with cognitive impairment and provide clini cal evidences for early prevention and treatment of type 2 diabetic patients with cognitive impairment.Methods A total of 211 cases of patients with type 2 diabetes were selected and their cognitive function had been assessed with the Chinese version of Montreal Cognitive Assessment(MOCA).Based on the MOCA scores,they were divided into the cognitive impairment group(CI) and the normal group(NC).Their gender,age,the level of education,the nature of the work,hypertension,body mass index,glycohemoglobin were recorded.Results There was a statistically significant difference(P 0.05) between the two groups in age,the level of education,body mass index,and glycohemoglobin.Multiple stepwise regression analysis showed that age,the level of education and the level of glycohemoglobin were in dependent risk factors for MOCA scores.Conclusion Elderly,high body mass index,and high glycated hemoglobin are risk factors for cognitive impairment in type 2 diabetic patients.Highly educated is a protective factor for cognitive impairment in type 2 diabetic patients.Effective controlling of blood glucose,body weight,and increasing the level of education may contribute to preventing or delaying the occurrence of cognitive impairment 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".