Bibliographic record
Abstract
PURPOSE OF REVIEW: Noncholesterol sterols (NCSs) in plasma encompass endogenous cholesterol precursors and exogenous phytosterols and cholesterol metabolites, which are used as surrogate measures of cholesterol synthesis and cholesterol absorption, respectively. The ratios of cholesterol synthesis to cholesterol absorption surrogates are also utilized to assess the overall balance of cholesterol metabolism, with higher values representing more synthesis and lower values more absorption. The objective of this review is to focus on recent findings using plasma NCSs and their potential in customizing dietary and pharmacological hypolipidemic therapies. RECENT FINDINGS: NCSs are often used to assess the impact of pharmacological and dietary interventions on cholesterol metabolism. Various forms of dyslipidemia have been characterized using NCSs, and NCSs may be a valuable tool in selecting appropriate treatment therapies. NCSs levels are affected by genetic, dietary and physiological factors and have been related to cardiovascular disease risk. SUMMARY: The expanded use of plasma NCSs is currently limited by the lack of standardized methodology. However, noncholesterol sterols are still a valuable research tool for the overall assessment of cholesterol metabolism and may have clinical potential in the personalization of diet and medicine.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".