The Prevalence And Risk Factors Of Cognitive Dysfunction In Patients With Diabetes Mellitus In Iraq
Bibliographic record
Abstract
Diabetes mellitus is a chronic metabolic disease that is distinguished by many complications. It is mainly a self-managed disease that needs intact cognition to maintain better quality of life. Cognitive dysfunction is a neurodegenerative changes that might be associated with diabetes mellitus. It is considered as the first stage of dementia and Alzheimer disease which is together with diabetes are global growing prevalence health concerns. This study investigates the unclear relationship between diabetes mellitus and cognitive dysfunction. It deals with occurrence of cognitive dysfunction among diabetes. It also compares the occurrenceof cognitive impairment in type 1 and type 2 diabetes. In addition, it investigates the influence of diabetes as a chronic disease, its complication and treatment on cognitive performance. A comparative cross-sectional methodology was adopted to achieve the study objectives. Two tools were used to evaluate cognitive dysfunction, the Mini-Mental Status Examination (MMSE), and Montreal Cognitive Assessment (MoCA). After calculating sample size, 380 patients with diabetes, and 100 control subjects who met inclusion and exclusion criteria were included in the study. As a sub-study, the association between brain magnetic resonance imaging (MRI) marker status and cognitive performance was assessed for certain number of participants (n=10 per arm). The major findings of this study are that according to MMSE, the prevalence of cognitive dysfunction was 16.3% of patients with diabetes and 7% of controls.
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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 | 0.001 |
| 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".