A dose-response and meta-analysis of phytosterols consumption on liver enzymes
Bibliographic record
Abstract
Purpose The results of human studies evaluating the efficacy of plant Phytosterols on liver function were inconsistent. Therefore, the purpose of this paper is to eliminate these controversies about the Phytosterols consumption on liver serum biochemistry in adult subjects. Design/methodology/approach The literatures systematically searched throughout PubMed and Scopus databases up to June 2018; it was conducted by using related keywords. Estimates of effect sizes were expressed based on weighted mean difference (WMD) and 95% CI from the random-effects model (erSimonian and Laird method). Heterogeneity across studies was assessed by using I2 index. Eighteen studies reported the effects of Phytosterols (PS) supplementation on liver serum biochemistry. Findings The current meta-analysis did not show a significant effect on ALT (MD: 0.165 U/L, 95% CI: −1.25, 1.58, p = 0.820), AST (MD: −0.375 IU/Liter, 95% CI: −1.362, 0.612, p = 0.457), ALP (MD: 0.804 cm, 95% CI: −1.757, 3.366, p = 0.538), GGT (MD: 0.431 U/L, 95% CI: −1.803, 2.665, p = 0.706) and LDH (MD: 0.619 U/L, 95% CI: −4.040, 5.277, p = 0.795) following PS consumption. Originality/value The authors found that no protective or toxic effects occur after the consumption of Phytosterols on liver enzymes including ALT, AST, ALP, LDH and GGT.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.058 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".