Factors Associated for Mild Cognitive Impairment in Older Korean Adults with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
The nature and extent of cognitive impairment was examined in 29 healthy elderly subjects (mean age 69.8 yr) with non-insulin-dependent diabetes mellitus (NIDDM) and 30 demographically similar nondiabetic community volunteers (mean age 68 yr).Measures of verbal learning, abstract reasoning, and complex psychomotor functioning were performed more poorly by diabetic than nondiabetic subjects.Conversely, there were no between-group differences in performance on tasks involving pure motor speed and simple verbal abilities.Within the diabetic group, individuals with poorer metabolic control performed more poorly on tasks involving learning, reasoning, and complex psychomotor performance, although this relationship was not evident for simple verbal or motor tasks.These data indicate that older people with NIDDM who are functioning well and perceive themselves as in good health are likely to manifest greater deficits than healthy elderly people in processing complex verbal or nonverbal material.Possible explanatory mechanisms are discussed, and directions for future research are explored.Diabetes Care 13:16-21, 1990A lthough the prevalence of non-insulin-dependent diabetes mellitus (NIDDM) increases with age, neither the clinical impact of this phenomenon nor the therapeutic approach to this patient population has been well defined.There are many
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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 | 0.001 |
| 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 | 0.001 |
| 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".