Emerging Therapies for Vascular Dementia and Vascular Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Cerebrovascular disease (CVD) and ischemic brain injury secondary to cardiovascular disease are common causes of dementia and cognitive decline in the elderly. CVD also contributes to cognitive loss in Alzheimer disease (AD). SUMMARY: Progress in understanding vascular cognitive impairment (VCI) and vascular dementia (VaD) has resulted in promising symptomatic and preventive treatments. Cholinergic deficits in VaD due to ischemia of basal forebrain nuclei and cholinergic pathways can be treated with cholinesterase inhibitors used in AD. Controlled clinical trials with donepezil and galantamine in patients with VaD, as well as in patients with AD plus CVD, have demonstrated improvement in cognition, behavior, and activities of daily living. The N-methyl-D-aspartate receptor antagonist memantine stabilized progression of VaD compared with placebo. Primary and secondary stroke prevention, in particular with control of hypertension and hyperlipidemia, can decrease VaD incidence. CONCLUSIONS: From a public health viewpoint, recognition of VCI before the development of dementia and correction of vascular burden on the brain may lead to a global decrease of incident dementia.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".