The effects of grape seed extract on glycemic control, serum lipoproteins, inflammation, and body weight: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
The aim of this systematic review and meta‐analysis was to analyze the effects of grape seed extract (GSE) on glycemic control and serum lipoproteins, inflammation and body weight. Two independent authors systematically searched online databases including EMBASE, Scopus, PubMed, Cochrane Library, and Web of Science from inception until May 30, 2019. Cochrane Collaboration risk of bias tool was applied to assess the methodological quality of included trials. The heterogeneity among the included studies was assessed using Cochrane's Q test and I‐square (I2) statistic. Data were pooled using a random‐effects model and weighted mean difference (WMD) was considered as the overall effect size. Fifty trials were included in this meta‐analysis. Pooling effect sizes from studies demonstrated a significant decrease in fasting plasma glucose (FPG) (WMD): −2.01; 95% confidence interval (CI): −3.14, −0.86), total cholesterol (TC; WMD: −6.03; 95% CI: −9.71, −2.35), low‐density lipoprotein (LDL) cholesterol (WMD: −4.97; 95% CI: −8.37, −1.57), triglycerides (WMD: −6.55; 95% CI: −9.28, −3.83), and C‐reactive protein (CRP) concentrations (WMD: −0.81; 95% CI: −1.25, −0.38) following GSE therapy. Grape seed did not influence HbA1c, HDL cholesterol levels, and anthropometric measurements. This meta‐analysis demonstrated that GSE intake significantly reduced FPG, TC, LDL cholesterol, triglycerides, and CRP levels.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".