Whole grapes or grape products on body weight, anthropometrics, and adipokines: systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
This meta-analysis aims to compile all randomised controlled trials (RCTs) that examined the effects of grapes or grape products on adult anthropometric measures and serum adipokines. We searched PubMed, Scopus, Google scholar, Web of Science and CENTRAL databases published before January 2022. Random-effects model was used to combine mean differences between intervention and placebo groups. A minimal reduction was revealed for BMI following consumption of grapes/grape products [weighted mean difference (WMD): −0.14 kg]; however, no significant effects were observed on body weight, except for trials conducting in female (n = 3 studies; WMD: −0.68 kg), and those enrolled patients with metabolic syndrome (n = 3 studies; WMD: −0.62 kg). No significant effect was found for waist circumference, body fat, waist to hip ratio, serum level of leptin and adiponectin. Our findings showed that grapes or grape products have no significant anti-obesity effects on body weight, anthropometric measures, or adipokines. However, BMI showed a trivial decrease, which should not be considered given the low quality of the studies.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.022 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".