Vascular contributions to cognitive impairment and dementia (VCID): A report from the 2018 National Heart, Lung, and Blood Institute and National Institute of Neurological Disorders and Stroke Workshop
Bibliographic record
Abstract
Vascular contributions to cognitive impairment and dementia (VCID) are characterized by the aging neurovascular unit being confronted with and failing to cope with biological insults due to systemic and cerebral vascular disease, proteinopathy including Alzheimer's biology, metabolic disease, or immune response, resulting in cognitive decline. This report summarizes the discussion and recommendations from a working group convened by the National Heart, Lung, and Blood Institute and the National Institute of Neurological Disorders and Stroke to evaluate the state of the field in VCID research, identify research priorities, and foster collaborations. As discussed in this report, advances in understanding the biological mechanisms of VCID across the wide spectrum of pathologies, chronic systemic comorbidities, and other risk factors may lead to potential prevention and new treatment strategies to decrease the burden of dementia. Better understanding of the social determinants of health that affect risks for both vascular disease and VCID could provide insight into strategies to reduce racial and ethnic disparities in VCID.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".