Is rifaximin better than nonabsorbable disaccharides in hepatic encephalopathy?
Bibliographic record
Abstract
BACKGROUND: The purpose of the present meta-analysis was to compare the efficacy of rifaximin and nonabsorbable disaccharides (NADs) in hepatic encephalopathy (HE). METHODS: After the registration of the present meta-analysis on INPLASY, all procedures were performed according to PRISMA 2020. Relevant literature was retrieved on PubMed, Embase, and the Cochrane Library up to September 5, 2021. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the enrolled studies, and Review Manager software (version 5.3) was used to analyze the clinical efficacy, blood ammonia and adverse effects. RESULTS: Six studies with 559 patients were included in the present meta-analysis. There were no significant differences in the basic characteristics of the included studies. Analysis of the complete resolution of HE showed that rifaximin was better than NADs (risk ratio [RR] = 1.87, 95% confidence interval [CI] = 1.03-3.39, P = .04). However, there were no significant differences in mental status (RR = 1.04, 95% CI = 0.92-1.18, P = .53), blood ammonia level (standard mean difference = -0.02, 95% CI = -0.40-0.02, P = .08), or drug adverse drug effects (OR = 0.43, 95% CI = 0.10-1.77, I2 = 56%, P = .24) between the rifaximin and NADs treatment groups. CONCLUSION: Rifaximin is not superior to NADs in the treatment of HE.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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".