Genetics of Vascular Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This review considers the current state of knowledge of genetic factors underlying vascular cognitive impairment (VCI). SUMMARY OF REVIEW: We argue here that genes conferring susceptibility to VCI must be of 2 nonmutually exclusive classes: (1) genes that confer susceptibility to cerebrovascular disease, and (2) genes that determine brain tissue responses to cerebrovascular disease (ie, render parenchymal tissue more or less susceptible to injury or able to repair itself after injury). Although some progress has been made in identifying genes of the first class, little has been done to explore genes of the second class. Evidence for the existence of such genes is presented. We discuss the advantages and disadvantages of different forms of cerebrovascular disease for studying these genes, and different study designs that might be used. CONCLUSIONS: The most critical challenge for genetic studies of VCI is to identify quantifiable phenotypes that can be reliably and effectively determined in large samples of subjects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".