The journey towards understanding lipoprotein(a) and cardiovascular disease risk: are we there yet?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Evidence continues to mount for an important role for elevated plasma concentrations of lipoprotein(a) [Lp(a)] in mediating risk of atherothrombotic and calcific aortic valve diseases. However, there continues to be great uncertainty regarding some basic aspects of Lp(a) biology including its biosynthesis and catabolism, its mechanisms of action in health and disease, and the significance of its isoform size heterogeneity. Moreover, the precise utility of Lp(a) in the clinic remains undefined. RECENT FINDINGS: The contribution of elevated Lp(a) to cardiovascular risk continues to be more precisely defined by larger studies. In particular, the emerging role of Lp(a) as a potent risk factor for calcific aortic valve disease has received much scrutiny. Mechanistic studies have identified commonalities underlying the impact of Lp(a) on atherosclerosis and aortic valve disease, most notably related to Lp(a)-associated oxidized phospholipids. The mechanisms governing Lp(a) concentrations remain a source of considerable dispute. SUMMARY: This article highlights some key remaining challenges in understanding Lp(a) actions and clinical significance. Most important in this regard is demonstration of a beneficial effect of lowering Lp(a), a development that is on the horizon as effective Lp(a)-lowering therapies are being tested in the clinic.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".