Biochemical and bioimaging markers for risk assessment and diagnosis in major cardiovascular diseases: a road to integration of complementary diagnostic tools
Bibliographic record
Abstract
This report from the first International Course on Integrated Biomarkers, Biochemical and Bioimaging Endpoints in Cardiovascular Diagnosis, Prevention, Therapy and Drug Development provides the basis for optimizing diagnostic, prognostic and therapeutic information in four areas of cardiovascular medicine: primary prevention of cardiovascular diseases, acute coronary syndromes, heart failure and stroke. Risk stratification and treatment strategies can be refined and enhanced through integration of bioimaging and biochemical markers to characterize sub-clinical and clinical atherosclerosis. For the integrative approach to be useful, each of the biomarkers must be validated and cost-effective. Clinical decision is the primary level of integration and is based on clinical evaluation and the use of a combination of bioimaging and biochemical markers. The decision to initiate preventive or therapeutic intervention must take into account the factors affecting the levels of expression of the biomarker and the potential input the biomarker has on metabolic processes or modulation of other biomarkers. The optimal approach to intervention must take into consideration the risk-benefit and cost-effectiveness ratios.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".