Current Concepts in Vascular Cognitive Impairment and Pharmacotherapeutic Implications
Bibliographic record
Abstract
OBJECTIVE: To review evolution of the vascular cognitive impairment (VCI) construct, including diagnosis, pharmacotherapeutic implications, and address challenges that will shape future developments. DATA SOURCES: Literature retrieval was accessed through PubMed, from 1966 to December 2008, using the terms vascular cognitive impairment, vascular dementia, post-stroke dementia, vascular cognitive disorder, mild cognitive impairment, criteria, disease progression, outcomes, treatment, prevention, biomarkers, and neuroimaging. STUDY SELECTION AND DATA EXTRACTION: All articles in published English identified from the data sources were evaluated for inclusion. Regarding pharmacotherapy, prospective double-blind, placebo-controlled studies were included as well as extensions or relevant post hoc analyses. DATA SYNTHESIS: In the 1970s, "senile dementia due to hardening of the arteries" was used to describe dementia due to vascular causes. This was a narrow view of what is now known to be a common form of cognitive impairment in older people. Multiple infarct dementia (MID) was first proposed to describe dementia attributable to multiple cerebral infarcts, followed by the vascular dementia (VaD) construct, itself meant to be an improvement over MID. The VaD construct had limitations, not the least of which was that, by the time a patient was diagnosed with VaD, the opportunity for prevention was lost. Thus arose the concept of VCI, representing a group of heterogeneous disorders that share presumed vascular causes. The importance of VCI is centered on the fact that vascular risk factors are treatable, and thus should lead to a reduction in the incidence of cognitive impairment due to vascular causes. There is evidence that treatment of hypertension can lead to a reduction in the incidence of cognitive impairment and dementia, and that treatment of VaD with acetylcholinesterase inhibitors may be beneficial. CONCLUSIONS: Careful attention needs to be given to controlling vascular risk factors in at-risk patients. Pharmacists should play an active role in this important area of geriatric pharmacotherapy.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".