A framework of biomarkers for vascular aging: a consensus statement by the Aging Biomarker Consortium
Bibliographic record
Abstract
Aging of the vasculature, which is integral to the functioning of literally all human organs, serves as a fundamental physiological basis for age-related alterations as well as a shared etiological mechanism for various chronic diseases prevalent in the elderly population. China, home to the world's largest aging population, faces an escalating challenge in addressing the prevention and management of these age-related conditions. To meet this challenge, the Aging Biomarker Consortium of China has developed an expert consensus on biomarkers of vascular aging (VA) by synthesizing literature and insights from scientists and clinicians. This consensus provides a comprehensive assessment of biomarkers associated with VA and presents a systemic framework to classify them into three dimensions: functional, structural, and humoral. Within each dimension, the expert panel recommends the most clinically relevant VA biomarkers. For the functional domain, biomarkers reflecting vascular stiffness and endothelial function are highlighted. The structural dimension encompasses metrics for vascular structure, microvascular structure, and distribution. Additionally, proinflammatory factors are emphasized as biomarkers with the humoral dimension. The aim of this expert consensus is to establish a foundation for assessing the extent of VA and conducting research related to VA, with the ultimate goal of improving the vascular health of the elderly in China and globally.
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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 | 0.157 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".