Vascular Biomarkers and Surrogates in Cardiovascular Disease
Bibliographic record
Abstract
Cardiovascular biomarker research efforts have resulted in the identification of new risk factors and novel drug targets, as well as the establishment of treatment guidelines. Government agencies, academic research institutions, diagnostic industries, and pharmaceutical companies all recognize the importance of biomarkers in advancing therapies to improve public health. In drug development, biomarkers are used to evaluate early signals of efficacy and safety, to select dose, and to identify the target population. The United States Food and Drug Administration has relied on biomarkers to support clinical applications in many therapeutic fields, including cardiovascular disease. The appropriate application of cardiovascular biomarkers requires an understanding of disease natural history, the mechanism of the intervention, and the characteristics and limitations of the biomarker. Channels of communication among researcher, developer, and regulator must remain open to maximize the success of future biomarker efforts. In 2003, 2004, and 2005, an international panel of cardiovascular biomarker experts convened at the "Cardiovascular Biomarker and Surrogate Endpoints Symposia" held in Bethesda, Md, to discuss the use of biomarkers in the development of improved cardiovascular diagnostics and therapeutics. The information presented in the present report summarizes the authors' perspective distilled from these proceedings.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".