The Brussels International Declaration on Lipoprotein(a) Testing and Management
Bibliographic record
Abstract
There is striking evidence that a high lipoprotein(a) [Lp(a)] concentration is a strong, independent, and causal cardiovascular risk factor. However, Lp(a) testing rates are very low (1 %-2 %) despite the fact that 1 in 5 individuals have elevated Lp(a) concentrations. The Brussels International Declaration on Lp(a) Testing and Management was co-created by the Lp(a) International Task Force and global leaders at the Lp(a) Global Summit, held in Brussels, Belgium, on March 24-25, 2025. The event, organized by FH Europe Foundation, brought together scientific experts, people with the lived experience of elevated Lp(a) and policy makers from the European Institutions and World Health Organization. The World Heart Federation, Global Heart Hub, and European Alliance for Cardiovascular Health and scientific organizations such as European Atherosclerosis Society, and International Atherosclerosis Society were formal partners. The Summit was hosted by a Member of the European Parliament, Romana Jerković, and held under the patronage of the Polish presidency of the Council of the European Union. The Declaration calls for 1) integration of Lp(a) testing and management into Global, European and National Cardiovascular Health Plans; 2) appropriate investment, policy and programmes in targeting Lp(a) testing and management based on a recent study demonstrating the substantial overall cost-saving to health systems across the globe; 3) political commitment to mandate systematic Lp(a) testing at least once during a person's lifetime, ideally at an early age, with full reimbursement; 4) incorporation of Lp(a) test results in the context of a person's cardiovascular risk assessment, with development of personalised cardiovascular health roadmaps as needed, without fear of dredit aiscrimination; 5) investment in public and healthcare professional education to increase awareness of Lp(a) and its impact on cardiovascular health.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.019 |
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".