Bibliographic record
Abstract
BACKGROUND AND PURPOSE: It is our premise that the pathophysiology of small vessel disease in the brain is similar to small vessel disease in other heavily perfused tissues and that the presence of small vessel disease elsewhere in the body foretells its presence in the brain as well as its consequences on cognitive function. The hypothesis presented in this article is that small vessel disease is a systemic condition of aging that is exacerbated by vascular risk factors, which results from dysfunction of arteriolar perfusion. This condition, which we term systemic arteriolar dysfunction, affects the brain as well as a number of extracranial systems. SUMMARY OF REVIEW: Recent literature is synthesized to suggest a possible etiology of this condition, highlighting the multiple pathways that may conspire to produce the endothelial and other vascular changes seen in systemic arteriolar dysfunction. CONCLUSIONS: Regardless of the etiology, we emphasize that small vessel disease is a systemic condition with major healthcare consequences, requiring a new paradigm in the way we practice medicine. Because this condition can be decelerated by control of vascular risk factors, doing so may significantly reduce morbidity, mortality, and healthcare costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".