Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the relevance of triglycerides to cardiovascular disease (CVD) risk. RECENT FINDINGS: Triglycerides are a commonly measured component of lipid profiles. Raised triglycerides are a component of the metabolic syndrome and are strongly associated with future risk of diabetes as well as cardiovascular disease. Triglyceride-rich particles form a component of cardiovascular risk above that delineated by low density lipoprotein (LDL) cholesterol. Elevated triglycerides are a marker of atherogenic small dense LDL, excess baseline and residual CVD risk even after statin therapy. Additional methods to lower triglycerides include niacin, fibrates and omega-3 fatty acids. Trials in monotherapy with both niacin and fibrates suggest some benefit in reducing CVD events based on evidence mostly derived from older studies. However, endpoint trials of adding either niacin or fenofibrate to statins have not shown any benefit, except possibly in patients with an increased atherogenic index (triglyceride : HDL-C ratio), or have been underpowered. Trials of omega-3 fatty acids have been performed at doses insufficient to affect lipid profiles in populations with inadequate control of LDL-C but did reduce CVD events. SUMMARY: Further trials of lipid-lowering agents beyond statins will be required in patients with LDL-C adequately controlled on statin therapy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".