Lipoprotein disorders and cardiovascular risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it