Effects of a diet high in plant sterols, vegetable proteins, and viscous fibers (dietary portfolio) on circulating sterol levels and red cell fragility in hypercholesterolemic subjects
Bibliographic record
Abstract
Plant sterols, soy proteins, viscous fibers, and nuts are advised for cholesterol reduction, but their combined effect on plant sterol absorption has never been tested. We assessed their combined action on serum sterols in hyperlipidemic subjects who were following low-saturated fat diets before starting the study and who returned to these diets post-test. The 1-mon test (combination) diet was high in plant sterols (1 g/1,000 kcal), soy protein (23 g/1,000 kcal), viscous fiber (9 g/1,000 kcal), and almonds (14 g/1000 kcal). Fasting blood was obtained for serum lipids and sterols, and erythrocytes were obtained for fragility prior to and at 2-wk intervals during the study. The combination diet raised serum campesterol concentrations by 50% and beta-sitosterol by 27%, although these changes were not significant after Bonferroni correction; near-maximal rises were found by the end of the first week, but no change was found in red cell fragility despite a 29% reduction in the LDL cholesterol level. No significant associations were observed between changes in red cell fragility and blood lipids or sterols. We conclude that plant sterols had a minimal impact on serum sterol concentrations or red cell fragility in hyperlipidemic subjects on diets that greatly reduced their serum lipids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".