Cognitive impairment in elderly type II Diabetes Mellitus and its associated risk factors / Rosnadia Suain Bon
Bibliographic record
Abstract
Cognitive impairment is common and may be part of normal ageing and may act as a precursor to dementia. There are several modifiable risk factors such as diabetes, dyslipidaemia and obesity which can promote the progression of dementia. This study aims to determine the prevalence of cognitive impairment and its associated factors and specific psychiatry disorders among elderly Type II Diabetes Mellitus patients in Hospital Kuala Lumpur.This is a cross sectional study involving 113 older adults more than 60 years old diagnosed as type II Diabetes Mellitus attending specialist physician clinic in Hospital Kuala Lumpur. The participants were recruited through universal sampling. Screening tools included demographic questionnaires, Montreal Cognitive Assessment-Malay ( MoCA), Depression Anxiety Stress Scale - Malay( DASS ) and MINI International Neuropsychiatry Interview - Malay ( M.I.N.I). The cut-off score for MoCA was taken as 22/23 for cognitive impairment. Descriptive analysis was performed and followed by multiple logistic regression. The prevalence of cognitive impairment was 46.9%. The prevalence for major depression and anxiety disorders were 10.6% and 2.7% respectively. Factors that were found to be significantly associated with cognitive impairment were Indian & Punjabi ethnicities (OR = 4.896, CI= 1.570-15.271), secondary education level ( OR = 0.343; CI = 0.122-0.962) and tertiary education level (OR = 0.045; CI = 0.008-0.257 ). Cognitive impairment was high among elderly type II diabetes mellitus and it was significantly associated with ethnicity and education level.
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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".