Prevalence and Predictors of Cognitive Decline Among Diabetes Mellitus Patients Attending Jinja Regional Referral Hospital: A Cross‐Sectional Study in Eastern Uganda
Bibliographic record
Abstract
Background and Aims: The burden of cognitive impairment (CI) is high in diabetes mellitus. CI can adversely affect the self-care and management of diabetes, which results in an increase in the risk of hypo- or hyperglycaemic events and diabetic complications. The study aimed to determine the prevalence and predictors of CI in diabetic patients. Methods: A hospital-based, cross-sectional study was conducted among diabetic patients who were attending Jinja Regional Referral Hospital (JRRH), Eastern Uganda, from April to June 2024. A pre-designed data collection tool was used to capture socio-demographics and clinic profiles of the participants. A Montreal Cognitive Assessment (MoCA), version 8.1, was used to assess the CI (score: ≥ 26 = normal, < 26 = cognitive impairment) in diabetic patients. We used a binary and multiple logistic regression analysis to identify predictors of CI in diabetes. Results: The prevalence of CI among diabetic patients was 63.11% (95% CI: 58.3-67.9), and it was high among Type II diabetic patients (66.96%). Most of the patients have mild CI (73.77%). Delayed recall (78.96%) and language (73.77%) cognitive domains were greatly affected. Variables like advanced age (AOR = 6.08; 95% CI = 2.05-18.03), education (Illiterate: AOR = 5.90; 95% CI = 2.16-16.14; primary: AOR = 17.07; 95% CI = 5.64-51.71), alcohol use (AOR = 2.56; 95% CI = 1.22-5.37), no physical activity (AOR = 5.24; 95% CI = 2.52-10.91), type II diabetes (AOR = 7.02; 95% CI = 2.17-22.64), duration of diabetes (5-10 years: AOR = 14.09; 95% CI = 5.75-34.55; > 10 years: AOR = 78.80; 95% CI = 23.79-260.95), uncontrolled blood glucose (AOR = 5.13; 95% CI = 1.91-13.83), hypertension (AOR = 5.26; 95% CI = 2.08-13.34), and diabetic complications (AOR = 4.30; 95% CI = 1.38-13.36) were significantly associated with CI among diabetic patients. Conclusion: The study concludes that more than half of the diabetic patients had CI. Factors such as age, education, alcohol use, physical activity, diabetes type, duration of diabetes, glycaemic control, hypertension, and diabetic complications were significantly associated with CI in diabetes. Therefore, the study recommends planning and implementing management strategies that focus on predictors of CI in diabetes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".