Bibliographic record
Abstract
INTRODUCTION: Ascites is a common complication of advanced cirrhosis that has a significant negative impact on survival. This review updates the reader on the medical management of ascites. AREAS COVERED: This review explores the pathophysiology of ascites formation in cirrhosis; the current mainstays of medical management (treating the underlying cause of cirrhosis, avoiding nephrotoxic agents, sodium restriction, and combination diuretic therapy); potential novel agents, such as vasoconstrictors and vaptans; and albumin infusions. The literature research covers all aspects of medical management of ascites from the English literature, concentrating on publications from the past 10 years. It provides a thorough understanding of how the correction of pathophysiology of ascites formation helps to improve ascites; knowledge on the monitoring of patients with cirrhosis and ascites receiving medical management, and on prophylaxis against potentially life-threatening complication such as spontaneous bacterial peritonitis; and potential new treatments for ascites. EXPERT OPINION: Management of patients with cirrhosis and ascites requires careful attention to fluid and electrolyte balance and avoidance of complications. Recognition of refractory ascites allows for the use of second-line treatments. All patients with cirrhosis and ascites should be considered for liver transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".