The relationships between atherosclerosis, heart disease, type 2 diabetes and dementia
Bibliographic record
Abstract
Type 2 diabetes in the elderly is associated with increased incidence of vascular disease, particularly, atherosclerosis of large blood vessels. Together with other risk factors such as dyslipidemia, atherosclerosis increases the risk for coronary heart disease and stroke. Most studies that have examined the impact of type 2 diabetes and other heart disease risk factors on cognitive functions do not provide evidence that heart disease risk factors (with the possible exception of triglycerides) further increase the likelihood of observing cognitive deficits in diabetic patients. However, none of these studies used imaging techniques to evaluate atherosclerosis or evidence of cerebrovascular disease, such as infarctions. The few studies that have included brain imaging suggest that evidence of cerebrovascular disease further increases the risk for dementia in diabetic patients. The results of longitudinal studies suggest that diabetes is an independent risk factor for cognitive decline and dementia. The pattern of neuropsychological performance observed in type 2 diabetic patients appears to be the result of multiple interacting processes developing over time. In addition to the detrimental effects of protracted impaired glucose regulation on the central nervous system, type 2 diabetes pathology also encompasses the detrimental effects of associated complications such as cerebrovascular disease, which is likely the main cause of the observed processing speed/reaction time decrements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".