Bibliographic record
Abstract
The purpose of this article is to review the basic and clinical science relating plasma triglycerides and cardiovascular disease. Although many aspects of the basic physiology of triglyceride production, its plasma transport, and its tissue uptake have been known for several decades, the relationship of plasma triglyceride levels to vascular disease is uncertain. Are triglyceride-rich lipoproteins, their influence on high-density lipoprotein and low-density lipoprotein, or the underlying diseases that lead to defects in triglyceride metabolism the culprit? Animal models have failed to confirm that anything other than early fatty lesions can be produced by triglyceride-rich lipoproteins. Metabolic products of triglyceride metabolism can be toxic to arterial cells; however, these studies are primarily in vitro. Correlative studies of fasting and postprandial triglycerides and genetic diseases implicate very-low-density lipoprotein and their remnants and chylomicron remnants in atherosclerosis development, but the concomitant alterations in other lipoproteins and other risk factors obscure any conclusions about direct relationships between disease and triglycerides. Genes that regulate triglyceride levels also correlate with vascular disease. Human intervention trials, however, have lacked an appropriately defined population and have produced outcomes without definitive conclusions. The time is more than ripe for new and creative approaches to understanding the relationship of triglycerides and heart disease.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".