Do gut microbiome‐targeted therapies improve liver function in cirrhotic patients? A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Microbiome-targeted therapies (MTTs) are considered as promising interventions for cirrhosis, but the impact of gut microbiome modulation on liver function and disease severity has not been fully assessed. We comprehensively evaluated the efficacy of MTTs in patients with liver cirrhosis. METHODS: Data from randomized controlled trials were collected through MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrial.gov from inception to February 20, 2023. Clinical outcomes were pooled and expressed in terms of risk ratios or mean differences (MD). Additional subgroup and sensitivity analyses were performed to validate the robustness of findings. A trial sequential analysis was applied to calculate the required information size and evaluate the credibility of the meta-analysis results. RESULTS: Twenty-one studies with a total of 1699 cirrhotic patients were included for meta-analysis. MTTs were associated with a significant reduction in aspartate aminotransferase (MD, -3.62; 95% CI, -6.59 to -0.65), the risk of hepatic encephalopathy (risk ratio = 0.56, 95% CI: 0.46 to 0.68), model for end-stage liver disease score (MD, -0.90; 95% CI, -1.17 to -0.11), ammonia (MD, -11.86; 95% CI, -16.39 to -7.33), and endotoxin (MD, -0.14; 95% CI, -0.23 to -0.04). The trial sequential analysis yielded reliable results of these outcomes. No effects were observed on the changes of other hepatic function indicators. CONCLUSION: MTTs appeared to be associated with a slowed deterioration in liver cirrhosis, which could provide reference for clinicians in treatment of cirrhotic patients based on their conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".