Perspectives on Cognitive Phenotypes and Models of Vascular Disease
Bibliographic record
Abstract
Clinical investigations have established that vascular-Associated medical conditions are significant risk factors for various kinds of dementia. And yet, we are unable to associate certain types of vascular deficiencies with specific cognitive impairments. The reasons for this are many, not the least of which are that most vascular disorders are multi-factorial and the development of vascular dementia in humans is often a multi-year or multi-decade progression. To better study vascular disease and its underlying causes, the National Heart, Lung, and Blood Institute of the National Institutes of Health has invested considerable resources in the development of animal models that recapitulate various aspects of human vascular disease. Many of these models, mainly in the mouse, are based on genetic mutations, frequently using single-gene mutations to examine the role of specific proteins in vascular function. These models could serve as useful tools for understanding the association of specific vascular signaling pathways with specific neurological and cognitive impairments related to dementia. To advance the state of the vascular dementia field and improve the information sharing between the vascular biology and neurobehavioral research communities, National Heart, Lung, and Blood Institute convened a workshop to bring in scientists from these knowledge domains to discuss the potential utility of establishing a comprehensive phenotypic cognitive assessment of a selected set of existing mouse models, representative of the spectrum of vascular disorders, with particular attention focused on age, sex, and rigor and reproducibility. The workshop highlighted the potential of associating well-characterized vascular disease models, with validated cognitive outcomes, that can be used to link specific vascular signaling pathways with specific cognitive and neurobehavioral deficits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".