Phytosterols in human nutrition: Type, formulation, delivery, and physiological function
Bibliographic record
Abstract
Abstract Phytosterols are a family of compounds similar to cholesterol which have been shown to lower cholesterol levels when supplemented in the diet. A daily dose of 2–3 g of phytosterols has been shown to reduce LDL‐cholesterol levels by 5–15%. Phytosterol supplementation can be undertaken using phytosterol enriched functional foods or nutraceutical preparations. The type of phytosterol supplemented, such as plant sterol or saturated plant stanol appear to be equally effective in lowering cholesterol levels. Phytosterols, whether in esterified or free form have both been shown to lower cholesterol levels, with esterified phytosterol formulations having a greater number of clinical trials demonstrating efficacy. The functional food or nutraceutical matrix which is used to deliver supplemental phytosterols can significantly affect cholesterol lowering efficacy. Effective cholesterol lowering by phytosterols depends on delivery of phytosterols to the intestine in a form which can compete with cholesterol for absorption. New phytosterol functional food and nutraceuticals products should always be tested to demonstrate adequate delivery of phytosterol dose and effective total and LDL‐cholesterol lowering. Phytosterol products which do not effectively lower cholesterol will negatively impact the perception and use of phytosterols, and must not be allowed on the marketplace.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".