The long journey of lipoprotein(a) from cardiovascular curiosity to therapeutic target
Bibliographic record
Abstract
Atherosclerosis, as the official journal of the European Atherosclerosis Society (EAS), decided that it would be timely to publish a comprehensive collection of review articles on lipoprotein(a). Spanning the last decade or two, this lipoprotein has become a further target in the fight against atherosclerotic cardiovascular disease. In that time, detailed knowledge about lipoprotein(a) has grown tremendously. Therefore, we decided not to have just one review article covering all aspects of lipoprotein(a), but rather to invite established experts in the field to write in-depth review articles on various aspects of lipoprotein(a). Collectively, these articles cover epidemiology, genetics, non-genetic influences, the influence of ethnicity, basic scientific investigations on the pathogenicity of lipoprotein(a), therapeutic developments to lower lipoprotein(a), and the challenging related to measurement of lipoprotein(a). The end result is a collection of 13 articles, which should be considered as the most comprehensive overview on the lipoprotein(a) field currently available. At the same time, the EAS invited the scientific community to submit original research papers in various areas of lipoprotein(a) research. This has resulted in an additional 15 articles that are part of an extended Atherosclerosis Special Issue along with the invited review articles.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".