Effects of curcumin in patients with non-alcoholic fatty liver disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Curcumin is an anti-inflammatory that is proposed to have a positive impact on patients with non-alcoholic fatty liver disease (NAFLD). We aim to assess the effects of curcumin in patients with NAFLD. Methods: Clinical trials from PubMed, Scopus, the Web of Science, and Cochrane CENTRAL with variables alanine transferase, aspartate transaminase, alkaline phosphatase, glycated hemoglobin (HBA1c), BMI, waist circumference, total cholesterol, total glycerides, high-density lipoproteins, and low-density lipoproteins were included. Homogeneous and heterogeneous were analyzed under a fixed-effects model and the random-effects model, respectively. Results: Fourteen clinical trials found that curcumin has no statistically significant effect on alanine transferase (MD = −2.20 [−6.03, 1.63], p = 0.26], aspartate transaminase (MD = 1.37 [−4.56, 1.81], p = 0.4), alkaline phosphatase (MD = 3.06 [−15.85, 9.73], p = 0.64), glycated hemoglobin (HBA1c), (MD = −0.06 [−0.13, 0.02], p = 0.16], and BMI (MD = 0.04 [−0.38, 0.46], p = 0.86). Curcumin reduced the waist circumference (MD = −4.87 [−8.50, −1.25], p = 0.008). Lipid profile parameters were not significant, except the total glycerides (MD = −13.22 [−24.19, −2.24], p = 0.02). Conclusions: Curcumin significantly reduces total glycerides and waist circumference in NAFLD.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".