Vascular stiffening and arterial complianceImplications for systolic blood pressure
Bibliographic record
Abstract
Hypertension in older persons is characterized by increased systolic blood pressure, and isolated systolic hypertension (ISH) is the most common type of hypertension in this population. It is thought that ISH results from age-associated vascular stiffening and reduced compliance that can be revealed and quantified by analysis of arterial pressure waveforms. Evaluation of pulse wave velocity and other related measures has shown that arterial stiffness increases with advancing age and other cardiovascular (CV) risk factors including hypertension, the metabolic syndrome, diabetes, obesity, hypercholesterolemia, and elevated C-reactive protein. Many of these relationships have been demonstrated in patients without clinical CV disease and are independent of patient age. Arterial stiffness is significantly and independently associated with both target organ damage and increased risk for CV morbidity and mortality. The mechanisms underlying age- and disease-related arterial stiffening are not fully understood. However, assessment of changes in gene expression associated with increased arterial stiffness and gene polymorphisms that increase the risk for vascular stiffening suggests that components of the renin-angiotensin system, matrix metalloproteinases, intracellular signaling, and extracellular matrix components may all be involved in this process. Interventions aimed at these targets may reduce vascular stiffness, lower systolic blood pressure, decrease the prevalence of ISH, and improve outcomes for patients (particularly older patients) with hypertension or other CV conditions.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".