Cognitive and affective functions in diabetic patients associated with diabetes‐related factors, white matter abnormality and aging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diabetes mellitus (DM) is associated with a decline in cognitive and affective functions. METHODS: In all, 182 outpatients with DM were investigated for associations of cognitive and affective functions with diabetes-related factors and cerebral white matter abnormalities. In addition, the difference in cognitive decline of age-matched late elderly normal subjects and DM patients was investigated. RESULTS: The present study revealed that cognitive and affective functions declined in some DM patients. Furthermore, the decline in these functions was unrelated to fasting blood sugar level but was related to glycosylated hemoglobin (HbA1c) and insulin resistance. Poor HbA1c control was associated with a significant decline in the 'calculation' subscale and insulin resistance for 'naming', 'read list of letters' and 'delayed recall' Montreal Cognitive Assessment (MoCA) subscale scores. Magnetic resonance imaging scans showed that both periventricular hyperintensity (PVH) and deep white matter hyperintensity were associated with Mini Mental State Examination (MMSE) and MoCA scores, but only PVH was related to homeostasis model assessment of insulin resistance scores. Compared with age-matched late elderly normal subjects, 'orientation to time' and 'registration' MMSE subscales declined in late elderly DM patients. CONCLUSIONS: These results suggest that cognitive and affective decline in DM patients was mostly related to glucose control and insulin resistance, whilst amongst late elderly subjects the impairment of 'attention' and 'orientation' were characteristic features of DM 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".