Bibliographic record
Abstract
Disorders of lipoproteins often lead to disease in humans. Most often the sequelae of long-term dyslipoproteinaemia lead to atherosclerotic vascular disease in all arterial beds. Plasma elevation of low-density lipoprotein cholesterol (LDL-C), very low-density lipoproteins (VLDL) and lipoprotein(a), and reduced levels of high-density lipoproteins (HDL-C) are risk factors for coronary artery disease. Severe elevations of plasma triglycerides may lead to acute pancreatitis. In Western societies and in emerging economies, lifestyle contributes to the expression of lipoprotein disorders. Many dyslipoproteinaemias have a genetic aetiology. This review will examine the contribution of genetic lipoprotein disorders in human disease. Emphasis will be placed on monogenic disorders that are associated with coronary artery disease and novel causes of disorders of high-density lipoproteins. The consideration of screening and treatment of affected individuals, especially children, must take into account the severity of the phenotype, the long-term risk of developing vascular disease and available evidence of clinical benefit in a group of diseases that are mostly asymptomatic until manifestations of organ ischaemia in the heart, limbs or brain.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".