Renal dysfunction in cirrhosis: diagnosis, treatment and prevention.
Bibliographic record
Abstract
Renal failure is a common complication of cirrhosis and is a poor prognostic indicator. Patients with severe liver dysfunction can develop HRS, characterized by a marked reduction in renal blood flow and hemodynamic disturbances. HRS is now subdivided into 2 types. Type 1 HRS carries a worse prognosis than type 2; these patients also do worse after liver transplantation. Precipitants of HRS need to be sought out and managed early. It is also important to rule out other organic causes of renal disease that can occur in these patients. The most common precipitants are bacterial infection, gastrointestinal bleeding, and aggressive paracentesis. Antibiotic prophylaxis should be used in patients with a history of SBP and in those admitted to hospital for gastrointestinal bleeding. Any nephrotoxic drugs should be removed and volume status of the patient should be maintained. Albumin infusions may be used in patients admitted with SBP and those undergoing large-volume paracentesis. Diuretic use in patients with ascites needs to be monitored, and these agents should be stopped if renal function worsens. Many treatment options are now showing promise for patients with HRS. Numerous studies have shown the benefit of terlipressin in this setting, with fewer side effects; however, there is also some evidence for the combination of midodrine and octreotide when terlipressin is not available. Intravenous albumin should be considered in adjunct. If there is no response to these therapies, TIPS or the molecular adsorbent recirculating system could be considered. Orthotopic liver transplantation is the most effective strategy for treatment of HRS. Unfortunately, some patients with HRS are not candidates for liver transplantation. For those patients who are to receive liver transplantation, their chances for survival are improved if their renal function is optimized before transplantation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".