Factors associated with poor glycaemic control in type 2 diabetic elderly patients with mildcognitive impairment
Bibliographic record
Abstract
Recently, data has indicated a higher incidence of mild cognitive impairment (MCI) in patients with diabetes. Old age is a risk factor for cognitive deterioration and dementia. The aim of the study was to find the factors associated with poor glycaemic control in type 2 diabetic elderly patients with MCI. Materials and method. A cross-sectional study was conducted on 87 diabetic patients with MCI in an outpatient clinic. All subjects were screened for MCI using the Montreal Cognitive Assessment (MoCA). Detailed medical history and collection of blood test samples were performed. Results. 83.9% of participants had poor glycaemic control. A positive correlation was found between HbA1c level and number of visit to a doctor per year, number of co-morbidities, duration of T2DM, triglycerides and fasting glucose level; and a negative correlation between HbA1c level and years of education, HDL cholesterol level and MoCA score. The univariate logistic regression models revealed factors which are associated with poor glycemic control are: less years of education, higher no of visit to doctor per year, increased number of co-morbidities, presence of CVD, retinopathy, higher levels of triglycerides and fasting glucose, lower level of HDL cholesterol, lower MoCA score. Multivariable model revealed that higher plasma levels of fasting glucose and triglycerides are significant predictors. Conclusions. There is a high prevalence of poor glycemic control patients among elderly diabetics with MCI. Higher plasma levels of fasting glucose and triglycerides seems to be the most important predictors of poor glycemic control, however father larger studies are needed to elucidate these relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".