Evolving guidelines on lipoprotein(a)
Bibliographic record
Abstract
PURPOSE OF REVIEW: Elevated plasma lipoprotein(a) [Lp(a)] is a causal and independent risk factor for atherosclerotic cardiovascular disease and an emerging therapeutic target. Over the past 15 years, many medical bodies from around the world have released scientific statements and clinical guidelines regarding Lp(a). This review tracks how recommendations on Lp(a) have evolved over this timeframe. RECENT FINDINGS: Powerful studies demonstrating the independent association of elevated Lp(a) in large numbers of patients have been published. The data allowed a more precise formulation of risk categories for Lp(a) levels and of models for how a given level of Lp(a) in a moderate-risk to high-risk primary prevention patient might inform management of modifiable risk factors such as LDL cholesterol. Guidelines and statements have increasingly recommended universal screening for elevated Lp(a) and have identified elevated Lp(a) as a risk-enhancing or amplifying factor. However, some gaps and inconsistencies remain. SUMMARY: Ongoing cardiovascular outcomes trials of potent Lp(a)-lowering therapies will inform clinical use of Lp(a) in the future. Presently, consensus is building for measurement of Lp(a) in all adults and for incorporation of Lp(a) levels into clinical decision-making for prevention of cardiovascular disease. However, caution is warranted as the evidence base underlying this consensus has several important missing pieces.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".