Reversal of hepatorenal syndrome type 1 with terlipressin plus albumin vs. placebo plus albumin in a pooled analysis of the <scp>OT</scp>‐0401 and <scp>REVERSE</scp> randomised clinical studies
Bibliographic record
Abstract
BACKGROUND: The goal of hepatorenal syndrome type 1 (HRS-1) treatment is to improve renal function. Terlipressin, a synthetic vasopressin analogue, is a systemic vasoconstrictor used for the treatment of HRS-1, where it is available. AIM: To compare the efficacy of terlipressin plus albumin vs. placebo plus albumin in patients with HRS-1. METHODS: Pooled patient-level data from two large phase 3, randomised, placebo-controlled studies were analysed for HRS reversal [serum creatinine (SCr) value ≤133 μmol/L], 90-day survival, need for renal replacement therapy and predictors of HRS reversal. Patients received intravenous terlipressin 1-2 mg every 6 hours plus albumin or placebo plus albumin up to 14 days. RESULTS: The pooled analysis comprised 308 patients (terlipressin: n = 153; placebo: n = 155). HRS reversal was significantly more frequent with terlipressin vs. placebo (27% vs. 14%; P = 0.004). Terlipressin was associated with a more significant improvement in renal function from baseline until end of treatment, with a mean between-group difference in SCr concentration of -53.0 μmol/L (P < 0.0001). Lower SCr, lower mean arterial pressure and lower total bilirubin and absence of known precipitating factors for HRS were independent predictors of HRS reversal and longer survival in terlipressin-treated patients. CONCLUSIONS: Terlipressin plus albumin resulted in a significantly higher rate of HRS reversal vs. albumin alone in patients with HRS-1. Terlipressin treatment is associated with improved renal function. (ClinicalTrials.gov identifier: OT-0401, NCT00089570; REVERSE, NCT01143246).
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".