The factors contributing to cognitive dysfunction in type 2 diabetic patients
Bibliographic record
Abstract
BACKGROUNDS: The aim of the research was to investigate the factors contributing to cognitive dysfunction in type 2 diabetic patients, to distinguish the complex relationship between diabetic retinopathy (DR) and different cognitive status. METHODS: Two hundred and ninety-seven type 2 diabetes mellitus (T2DM) patients were enrolled in our study. We adopted the Clinical Dementia Rating (CDR), Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MOCA) to evaluate the cognitive function. Firstly, cognition status was classified into dementia and non-dementia according to MMSE and CDR. Patients with non-dementia were further classified into mild cognitive impairment (MCI) and normal cognition status based on MOCA. The factors contributing to cognitive dysfunction were analyzed. RESULTS: Among the 297 T2DM subjects, 47 were enrolled in the dementia group and 174 in the MCI group according to a battery of cognitive function tests, presenting a prevalence of 15.8% and 58.6% respectively. After adjustment for age, sex, and education level, waist circumference and DR were risk factors for dementia (OR: 1.057, P=0.011; OR: 2.197, P=0.040). Low-density lipoprotein cholesterol (LDL-C) was a risk factor for MCI (OR: 1.635, P=0.047), while age at T2DM onset and moderate drinking were protective factors for MCI (OR: 0.936, P=0.044; OR: 0.289, P=0.004). CONCLUSIONS: MCI is common in T2DM patients. Waist circumference and DR are risk factors of dementia, LDL-C is a risk factor for MCI, and moderate drinking and age at T2DM onset are protective factors for MCI. DR is unrelated to MCI in T2DM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".