Assessment of cognitive disorders in elderly type 2 diabetic patients with obesity
Bibliographic record
Abstract
Background Cognitive impairment is common in the elderly and can result from many chronic illnesses. The risk of CI is around one-third for older persons with diabetes. Diabetes increases the incidence of numerous common geriatric syndromes in older persons, including polypharmacy, CI, depression, urine incontinence, injurious falls, and chronic pain. Additionally, obesity raises the risk of high blood pressure, and DM and promotes arterial aging, which are known to be independent risk factors for CI.Objective The current study evaluated cognitive disorders in elderly type 2 diabetic patients with obesity.Material and Methods A sample of 120 elderly persons was divided into 3 groups: Group (1): 40 elderly non-obese non-diabetics (Control), Group (2): 40 elderly obese non-diabetic patients, and Group (3): 40 elderly obese type 2 diabetic patients. Fasting plasma glucose (FPG), 2 hPPPG (2 hour post prandial plasma glucose), glycosylated hemoglobin (HA1C) were measured. Obesity was assessed using height, weight, body mass index (BMI), and waist circumference (WC). Cognitive impairment in the elderly was detected using a questionnaire called the Montreal Cognitive Assessment (MoCA).Results There was statistical significance between the three investigated groups concerning HbA1c, FPG, and 2-hour PPG. Also, there was statistical significance between the three study groups in terms of total MOCA score. Weight, BMI, and WC showed statistical significance between the three study groups.Conclusion: Cognitive impairment is related to obesity and type 2 diabetes in elderly.
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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 | 0.001 |
| 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".