Systematic review: renal and other clinically relevant outcomes in hepatorenal syndrome trials
Bibliographic record
Abstract
BACKGROUND: Although reversal of pretransplant renal dysfunction in hepatorenal syndrome reduces post-transplant complications, the overall impact on morbidity and mortality requires clarification. AIM: To review trials of pharmacologic interventions in hepatorenal syndrome, with specific assessment of trial quality and study endpoints, including patient survival and renal outcome measures. METHODS: Literature search and selection was carried out by a single reviewer. Data extraction and quality analysis were carried out by two independent reviewers. RESULTS: Of 848 identified articles, 36 were eligible for inclusion. Twenty-one were full-text. Only 19% were randomized-controlled trials. About 50% of studies included only Type 1 hepatorenal syndrome patients. Serum creatinine, urine output and urine sodium were the most common renal outcome measures. Only 42% defined a primary renal endpoint. About 88% of articles reported mortality rates. CONCLUSIONS: Existing literature of pharmacologic agents for use in hepatorenal syndrome is limited by poor study design, including non-randomization, heterogeneous study populations, lack of power, and limited use of clinically relevant outcomes. There is insufficient information in most trials to judge the impact of pharmacologic therapy on mortality or rates of transplantation. The validity of renal outcome measures as surrogate markers of more clinically relevant endpoints has not been established.
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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 | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".