Prevalence of Cognitive Impairment among Type 2 Diabetes Mellitus Patients Attending Family Medicine Clin-ic in Suez Canal University Hospital
Bibliographic record
Abstract
Background: Dementia risk is increased by 50% in people with type 2 diabetes mellitus (T2DM). The gradual loss of most cognitive functions leads to increased dependency and social isolation. Aim: This study aimed to assess the prevalence of cognitive impairment among T2DM patients compared to non-diabetic patients and to determine the associated factors that increase the risk of cognitive impairment among T2DM patients. Subjects and Methods: A comparative cross-sectional study was conducted at the family medicine outpatient clinic, Suez Canal University Hospital, Egypt, between October 2019 and October 2020. A simple random sampling of 400 participants was categorized into two groups, T2DM patients (200) and non-diabetic patients (200). The Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) tools were used to assess the cognitive function. Results: The prevalence of cognitive impairment was 50% in diabetic patients as measured by MoCA,compared to 26.5 % in the non-diabetic group (P <0.05). In specific cognitive domains (orientation, calculation, recall, and language), diabetic patients showed significantly lower scores compared with non-diabetic patients (P <0.05). Education and socioeconomic status were significant positive predictors of MMSE score; while age, BMI, duration of diabetes, FBG, HbA1c, and LDL were negative predictors of cognitive impairment tested by MMSE among T2DM patients(p < 0.05). Conclusion: Diabetic patients were more likely to have cognitive impairment compared to patients without diabetes. Diabetes had a particularly negative impact on the following cognitive functions: orientation, calculation, recall, and language.
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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 | 0.000 |
| 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) | 0.002 | 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".