A study to assess the relationship between cognitive impairment and glycemic management in patients with type 2 diabetes using MoCA score
Bibliographic record
Abstract
Background: Later in life, people with diabetes mellitus 2 experience cognitive impairment, which lowers their quality of life. There is little local literature on cognitive diseases, particularly mild cognitive impairment (MCI), despite mounting evidence of these conditions. Materials and Methods: We used the Montreal Cognitive Assessment (MoCA) test to determine the prevalence of mild cognitive impairment (MCI), which was previously unknown, in type 2 diabetic patients who were visiting a tertiary care centre. We also looked at the relationships between the MoCA scores and HbA1c, fasting blood sugar (FBS), postprandial blood sugar (PPBS), age, and length of diabetes. The study comprised seventy individuals with type 2 diabetes mellitus. Normal cognition (NC) was defined as patients with MoCA scores ≥26, while MCI was defined as those with scores <26. Results: While 55% of individuals with type 2 diabetes mellitus had MCI (MoCA score <26), 50 or 45% of those patients had normal cognitive function (MoCA score ≥26). Patients with mild cognitive impairment had significantly higher fasting, PPBS, and HbA1c levels. The groups' mean ages and the length of time they had diabetes did not differ significantly. The MoCA scores were negatively correlated with the levels of HbA1c, FBS, and 2hr PPBS Conclusion: According to the study's findings, individuals with type 2 diabetes are very susceptible to mild cognitive impairment. There was a negative correlation between the MoCA score and HbA1c, higher disease duration, and fasting blood sugar levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".