P1‐401: The Correlation of Diabetic Status, Ischemic and Atrophic Burdens on Brain MRI and Cognitive Decline in Seventh Decade Diabetic Patients with Cognitive Impairment: 1‐Year Prospective, Observational Study
Bibliographic record
Abstract
Although the increasing number of clinical researches about diabetes and cognition, many limitations and debates have been exposed and yet revealed little. Also the contribution of Alzheimer-type and/or vascular pathology to cognitive declines has been remained unclear. The aim of this study was to evaluate the contributing factors correlated with cognitive declines in selected diabetic patients with cognitive impairments prospectively. After interviewing 286 diabetic patients using dementia screening questionnaire in their 7decades, we enrolled 49 subjects who have cognitive impairment (age=64.76±3.27(61-70), M:F=26:23, education=7.74±4.53 years, K-MMSE= 25.37±3.92, MoCA=18.24±4.69). Korean version mini-mental status examination (K-MMSE), MoCA and several laboratory examination of diabetes and lipid were tested and repeated after 6 and 12 months. All subjects were performed Brain MRI and scored visually focusing ischemia and atrophy. The fluctuation index of fasting blood glucose(FBS) and glycosylated hemoglobin(HbA1c) were negatively correlated with cognitive change (p=0.01, p=0.02). And low density lipoprotein(LDL) level was negatively correlated with cognitive change(p=0.02) but high density lipoprotein(HDL) was positively(p=0.03). MRI factors focusing on white matter hyperintensities and medial temporal atrophy are not significantly correlated with cognitive declines. We concluded the fluctuation rather than mean value of blood glucose level are the possible predictor of cognitive declines in diabetic patients and suggested management strategy. There is a need for larger, quantitative, clinical-neuroimaging studies to improve knowledge of the complex contributions by vascular and Alzheimer pathologies in diabetic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".