Bibliographic record
Abstract
Niacin is an important vitamin (B3) that can be used in gram doses to positively modify pathogenetically relevant lipid disorders: elevated LDL cholesterol, elevated non-HDL cholesterol, elevated triglycerides, elevated lipoprotein(a), and reduced HDL cholesterol. This review reports the latest published findings with respect to niacin's mechanisms of action on these lipids and its anti-inflammatory and anti-atherosclerotic effects. In the pre-statin era, niacin was shown to have beneficial effects on cardiovascular end-points; but in recent years, two major studies performed in patients whose LDL cholesterol levels had been optimized by a statin therapy did not demonstrate an additional significant effect on these end-points in the groups where niacin was administered. Both studies have several drawbacks that suggest that they are not representative for other patients. Thus, niacin still plays a role either as an additive to a statin or as a substitute for a statin in statin-intolerant patients. Moreover, patients with elevated triglyceride and low HDL cholesterol levels and patients with elevated lipoprotein(a) concentrations will possibly benefit from niacin, although currently the study evidence for these indications is rather poor. Niacin may be useful for compliant patients, however possible side effects (flushing, liver damage) and contraindications should be taken into consideration.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".