Vitamin D Deficiency as a Factor Associated with Cognitive Impairment in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Objective: Vitamin D as an essential nutrient is increasingly being studied and reported to have roles in diabetes and cognitive function through its antioxidant, anti-inflammatory, and neuroprotective functions. This study aimed to investigate vitamin D deficiency as a factor associated with cognitive impairment in Type 2 Diabetes Mellitus patients. Materials and Methods: This case-control study was conducted at the diabetic center and neurology outpatient clinic at Prof. Dr. I.G.N.G Ngoerah Hospital in Denpasar, Indonesia between September and December 2022. Cases had a score of < 26 on the Montreal Cognitive Assessment questionnaire (Indonesian version) controls had a score ≥26. Vitamin D levels were assessed using serum 25-hydroxyvitamin D levels. The cut-off for vitamin D deficiency was obtained through the receiver operating curve characteristic. Results: In total 31 cases and 31 controls were included. The cut-off for vitamin D deficiency was <24.6 ng/ml. Patients with T2DM and vitamin D deficiency had an increased association with cognitive impairment (OR 3.8; 95% CI [1.1 to 13.4]) compared to patients without vitamin D deficiency. Other independent factors associated with cognitive impairment in T2DM were low education levels (OR 5.4; 95% CI [1.3 to 22.2]) and diabetes duration of more than 5 years (OR 4.1; 95% CI [1.1 to 14.4]). Conclusion: Vitamin D deficiency is one of the factors associated with cognitive impairment in T2DM patients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".