Screening for Cognitive Disorders in Elderly Diabetics
Bibliographic record
Abstract
Introduction Old people with diabetes are more likely to develop cognitive impairment, Alzheimer's disease and vascular dementia. However, the determinants of the association between diabetes and cognitive impairments are only partially known. Objectives To evaluate cognitive disorders in elderly diabetic patients and to identify risk factors of cognitive impairment in this population. Methods It was a cross-sectional study. It involved outpatients aged 65 and older, who were followed for diabetes in the endocrinology department at the Hedi Chaker University Hospital in Sfax (Tunisia), from October 1 to December 31, 2015. For each patient, we collected sociodemographic, clinical and therapeutic data. We used the Montreal Cognitive Assessment (MoCA) to identify mild cognitive decline (score < 26/30). Results We identified 70 patients, all with type 2 diabetes. The average age was 66.8 years. The sex ratio (M: F) was 0.7. The mean duration of diabetes was 14.76 years. The average MoCA score was 20.68 ± 6. Forty patients (57%) had cognitive decline. The cognitive impairment was statistically correlated with female sex (P = 0.02), low level of education (P = 0.00), high levels of glycated hemoglobin (Hb A1c ≥ 7%) (P = 0.00), presence of hypoglycemic episodes (P = 0.05) and presence of dyslipidemia (P = 0.00). Conclusion Our study confirmed the high rate of cognitive decline in older type 2 diabetes patients. The profile of subjects at risk was consistent with the literature: poorly controlled diabetes, severe recurrent hypoglycaemia and associated dyslipidemia. Acting on these risk factors would prevent cognitive decline and therefore progression to dementia. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 | 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".