Effects of garlic supplementation on liver enzymes: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Current evidence on the beneficial effects of garlic on liver enzymes is contradictory. Therefore, the aim of this systematic review and meta-analysis is to evaluate the effect of garlic supplementation on human liver enzymes, such as Alanine Transaminase (ALT/SGPT) and Aspartate Transaminase (AST/SGOT). To collect the required data, PubMed, Scopus, ISI Web of Science, and Google scholar databases were systematically searched from inception to June 2019. A meta-analysis was conducted using the random-effects model to evaluate the effects of garlic supplementation on ALT and AST levels. The Cochran's Q-test and inconsistency index were also used to evaluate heterogeneity among the studies. Among a total of 15,514 identified articles, six studies (containing 301 participants) met the inclusion criteria. Results of the meta-analysis showed that garlic supplementation significantly decreased AST level (Hedges' g = -0.36, 95% confidence interval [CI]: -0.72, -0.004, p = .047); whereas, it had no significant effect on ALT level (Hedges' g = -0.22, 95% CI: -0.64, 0.20, p = .310). Results showed that garlic supplementation reduced AST levels significantly; however, had no significant effect on ALT levels. Further studies are still needed to confirm the results.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".