Gut microbiome-targeted therapies in liver cirrhosis: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Microbiome-targeted therapies (MTTs), including probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT), have been proposed as a potential treatment for cirrhosis via modulation of gut microbiome, while the impact of gut microflora alteration on liver function in cirrhosis trajectory is unclear, and no related systematic review has been published. We aim to comprehensively assess the effects of MTTs in patients with liver cirrhosis. Methods We will search databases of MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) with no time restriction. Only randomized controlled trials published in English will be included. Two independent reviewers will be responsible for study identification and selection, data extraction, and risk of bias assessment, with discrepancies resolved by consensus or referral to a third author. Heterogeneity of studies will be examined using Cochrane Q-test and I2 statistics. The data will be pooled using either a fixed- or random-effects model based on I2 statistics. The results will be presented as risk ratios (RR) or mean differences (MD) with 95% confidence intervals (CI). We will perform subgroup analysis on the type of MTTs and assess the reporting biases. Sensitivity analysis will be conducted to test the stability of each outcome result. Discussion There is no current study about the role of MTTs in developing the liver function, and the therapeutic effects of MTTs are inconsistent. By investigating the liver-specific indicators when treating with multiple MTTs on course of cirrhosis, our findings will give more conclusive and stronger evidence about the efficacy of MTTs and provide new insight into the action mechanisms of these MTTs. Systematic review registration PROSPERO CRD42021253198.
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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 | 0.070 | 0.116 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.032 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".