Report of the National Heart, Lung, and Blood Institute Workshop on Lipoprotein(a) and Cardiovascular Disease: Recent Advances and Future Directions
Bibliographic record
Abstract
It has been estimated that approximately 37% of the US population judged to be at high risk for developing coronary artery disease (CAD), based on the National Cholesterol Education Program guidelines, have increased plasma lipoprotein(a) [Lp(a)], whereas Lp(a) is increased in only 14% of those judged to be at low risk. Therefore, the importance of establishing a better understanding of the relative contribution of Lp(a) to the risk burden for CAD and other forms of vascular disease, as well as the underlying mechanisms, is clearly evident. However, the structural complexity and size heterogeneity of Lp(a) have hindered the development of immunoassays to accurately measure Lp(a) concentrations in plasma. The large intermethod variation in Lp(a) values has made it difficult to compare data from different clinical studies and to achieve a uniform interpretation of clinical data. A workshop was recently convened by the National Heart, Lung, and Blood Institute (NHLBI) to evaluate our current understanding of Lp(a) as a risk factor for atherosclerotic disorders; to determine how future studies could be designed to more clearly define the extent to which, and mechanisms by which, Lp(a) participates in these processes; and to present the results of the NHLBI-supported program for the evaluation and standardization of Lp(a) immunoassays. This report includes the most recent data presented by the workshop participants and the resulting practical and research recommendations.
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 | 0.031 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".