A Randomized, Placebo-Controlled, Double-Blind Crossover Study to Assess a Unique Phytosterol Ester Formulation in Lowering LDL Cholesterol Utilizing a Novel Virtual Tracking Tool
Bibliographic record
Abstract
Elevated blood concentration of low-density lipoprotein cholesterol (LDLc) is a primary risk factor for developing cardiovascular disease. Lifestyle interventions including an increase in dietary phytosterols as well as medications have proven effective in lowering LDLc. The primary objective of this randomized, placebo controlled, double blind, crossover study was to determine the impact of a new phytosterol emulsion for dietary supplements (1.5 g/day phytosterol equivalents) on LDLc concentrations. Thirty-two healthy adults were randomly assigned to receive placebo or treatment followed by a washout period, followed by placebo or treatment, each phase lasting one month. Secondary endpoints related to cardiovascular health were also assessed. Study management, including screening, recruitment, monitoring, compliance, and data collection, were done remotely (a siteless clinical trial) utilizing a novel virtual tool. Phytosterol supplementation significantly lowered LDLc concentrations by 10.2% (16.17 mg/dL or 0.419 mmol/L, p = 0.008 by paired t-test, p = 0.014 by Wilcoxon signed rank testing). No secondary biomarkers were found to change significantly. Supplementation with phytosterols in a new dietary supplement formulation efficiently and safely decreases LDLc within one month in a free-living setting.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".