The Comparative Effectiveness of Vasoactive Treatments for Hepatorenal Syndrome: A Systematic Review and Network Meta-Analysis*
Bibliographic record
Abstract
OBJECTIVE: Hepatorenal syndrome (HRS) is associated with high rates of morbidity and mortality. Evidence examining commonly used drug treatments remains uncertain. We assessed the comparative effectiveness of inpatient treatments for HRS by performing a network meta-analysis of randomized clinical trials (RCTs). DATA SOURCES: We searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, Medline In-Process & Other Non-Indexed Citations, Scopus, and Web of Science from inception. STUDY SELECTION AND DATA EXTRACTION: Pairs of reviewers independently identified eligible RCTs that enrolled patients with type 1 or 2 HRS. Pairs of reviewers independently extracted data. DATA SYNTHESIS: We assessed risk of bias using the Cochrane tool for RCTs and certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluations approach. Our main outcomes are all-cause mortality, HRS reversal, and serious adverse events. Of 3,079 citations, we included 26 RCTs examining 1,736 patients. Based on pooled analysis, terlipressin increases HRS reversal compared with placebo (142 reversals per 1,000 [95% CI, >87.7 to >210.9]; high certainty). Norepinephrine (112.7 reversals per 1,000 [95% CI, 52.6 to >192.3]) may increase HRS reversal compared with placebo (low certainty). The effect of midodrine+octreotide (67.8 reversals per 1,000 [95% CI, <2.8 to >177.4]; very low) on HRS reversal is uncertain. Terlipressin may reduce mortality compared with placebo (93.7 fewer deaths [95% CI, 168.7 to <12.5]; low certainty). Terlipressin probably increases the risk of serious adverse events compared with placebo (20.4 more events per 1,000 [95% CI, <5.1 to >51]; moderate certainty). CONCLUSIONS: Terlipressin increases HRS reversal compared with placebo. Terlipressin may reduce mortality. Until access to terlipressin improves, initial norepinephrine administration may be more appropriate than initial trial with midodrine+octreotide. Our review has the potential to inform future guideline and practice in the treatment of HRS.
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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".