Impact of nutraceuticals on markers of systemic inflammation: Potential relevance to cardiovascular diseases – A position paper from the International Lipid Expert Panel (ILEP)
Bibliographic record
Abstract
Inflammation is a marker of arterial disease stemming from cholesterol-dependent to -independent molecular mechanisms. In recent years, the role of inflammation in atherogenesis has been underpinned by pharmacological approaches targeting systemic inflammation that have led to a significant reduction in cardiovascular disease (CVD) risk. Although the use of nutraceuticals to prevent CVD has largely focused on lipid-lowering (e.g, red-yeast rice and omega-3 fatty acids), there is growing interest and need, especially now in the time of coronavirus pandemic, in the use of nutraceuticals to reduce inflammatory markers, and potentially the inflammatory CVD burden, however, there is still not enough evidence to confirm this. Indeed, diet is an important lifestyle determinant of health and can influence both systemic and vascular inflammation, to varying extents, according to the individual nutraceutical constituents. Thus, the aim of this Position Paper is to provide the first attempt at recommendations on the use of nutraceuticals with effective anti-inflammatory properties.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".