New challenge of hepatorenal syndrome: Prevention and treatment
Bibliographic record
Abstract
Hepatorenal syndrome (HRS) remains one of the major therapeutic challenges in hepatology today. The pathogenesis is complex, but the final common pathway seems to be that sinusoidal portal hypertension, in the presence of severe hepatic decompensation, leads to splanchnic and systemic vasodilatation and decreased effective arterial blood volume. Renal vasoconstriction increases concomitantly, renal hemodynamics worsens, and renal failure occurs. Renal failure was shown 15 years ago to be potentially reversible after liver transplantation. This potential reversibility together with increased understanding of the pathogenesis has led to successful preliminary attempts to reverse HRS nonsurgically with combinations of splanchnic vasoconstrictors and colloid volume expansion, insertion of transjugular intrahepatic portovenous shunt radiologically, and improved forms of dialysis. Recent classification of HRS into the acute onset or severe type 1 with virtually 100% mortality and the more insidious less severe type II promises to shed more light on the pathogenesis of HRS, especially on the currently unrecognized precipitating factors. It is hoped that this classification will be included in the necessary and carefully performed clinical trials, which should lead to clearer indications for the available therapies. The challenge now is to use all this information to improve our management of cirrhotic patients to prevent occurrence of HRS in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".