Frequent questions and responses on the 2022 lipoprotein(a) consensus statement of the European Atherosclerosis Society
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2022, the European Atherosclerosis Society (EAS) published a new consensus statement on lipoprotein(a) [Lp(a)], summarizing current knowledge about its causal association with atherosclerotic cardiovascular disease (ASCVD) and aortic stenosis. One of the novelties of this statement is a new risk calculator showing how Lp(a) influences lifetime risk for ASCVD and that global risk may be underestimated substantially in individuals with high or very high Lp(a) concentration. The statement also provides practical advice on how knowledge about Lp(a) concentration can be used to modulate risk factor management, given that specific and highly effective mRNA-targeted Lp(a)-lowering therapies are still in clinical development. This advice counters the attitude: "Why should I measure Lp(a) if I can't lower it?". Subsequent to publication, questions have arisen relating to how the recommendations of this statement impact everyday clinical practice and ASCVD management. This review addresses 30 of the most frequently asked questions about Lp(a) epidemiology, its contribution to cardiovascular risk, Lp(a) measurement, risk factor management and existing therapeutic options.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it