Bibliographic record
Abstract
The use of terlipressin in the treatment of hepatorenal syndrome type 1 (HRS-1) in patients with advanced cirrhosis wait-listed for liver transplant (LT) has been controversial. Successful treatment lowers patients' Model for End-Stage Liver Disease (MELD) score and hence their LT priority. Terlipressin's potential ischemic side effects and risks for respiratory failure in susceptible patients lend support to directly proceed to LT. However, responders to terlipressin have better post-LT survival with lower incidences of post-LT chronic kidney disease and need for renal replacement therapy (RRT). Available data suggest that terlipressin responders have not all been impacted negatively. HRS-1 itself confers a greater negative effect on survival when compared with patients with the same MELD score but without HRS-1; therefore, various countries except the United States have strategies to preserve the wait-list position of terlipressin responders. The MELD lock strategy uses the patient's pre-terlipressin MELD score to maintain their wait-list position indefinitely; a modified MELD lock system requires re-evaluation of the patient's eligibility status every 3 months. Patients taking long-term terlipressin for recurrent HRS are treated as needing RRT in assessing their LT priority. The United States considers that more data are needed before devising its own system for managing wait-listed terlipressin responders. Current data suggest that treating and reversing HRS in wait-listed patients is the appropriate course of action. This article will review the pros and cons of using terlipressin in LT wait-listed patients with HRS and the various strategies practiced by different countries to ensure equitable access to LT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".