Cognitive Impairment and Its Associated Risk Factors in the Elderly With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Cognitive impairment is not only common but may also act as a precursor for dementia. Moreover, diabetes mellitus has been shown to promote the progression of dementia. This study aims to determine the prevalence of cognitive impairment and its associated risk factors among the elderly patients having type 2 diabetes mellitus (T2DM) in Hospital Kuala Lumpur, Malaysia. This is a cross-sectional study involving 113 participants who were attending the physician clinic. The participants were selected using the universal sampling method. The tools included a sociodemographic questionnaire, the Montreal Cognitive Assessment, the Depression Anxiety Stress Scale, and the Mini-International Neuropsychiatry Interview. Descriptive analysis was performed and followed by multiple logistic regression. The prevalence of cognitive impairment, depressive disorder, and anxiety disorder was 46.9, 10.6, and 2.7%, respectively. Indians (aOR = 4.90, 95% CI = 1.57-15.27) as well as participants who had completed their secondary (aOR = 0.34; 95% CI = 0.12-0.96) and tertiary (aOR = 0.05; 95% CI = 0.01-0.26) levels of education were significantly associated with cognitive impairment. The prevalence of cognitive impairment was high as well as significantly associated with the ethnicity and education levels of the elderly participants. Indeed both secondary and tertiary education decreased the odds ratio of developing cognitive impairment when compared with no formal and primary education. To determine and reduce the potential risk factors which contribute to cognitive impairment, an early diagnosis of T2DM is crucial. The early detection and treatment of T2DM as well as its associated risk factors are key factors in protecting against cognitive impairment.
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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 | 0.000 | 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) | 0.001 | 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".