Role of point-of-care ultrasound (POCUS) in clinical hepatology
Bibliographic record
Abstract
Hospitalized patients with cirrhosis frequently require critical care management for sepsis, HE, respiratory failure, acute variceal bleeding, acute kidney injury (AKI), shock, and optimization for liver transplantation, while outpatients have unique care considerations. Point-of-care ultrasonography (POCUS) enhances bedside examination of the hepatobiliary system and relevant extrahepatic sites. POCUS includes cardiac US and is used to assess volume status and hemodynamic parameters like cardiac output, systemic vascular resistance, cardiac contractility, and pulmonary artery pressure, which aid in the early and accurate diagnosis of heart failure, cirrhotic cardiomyopathy, porto-pulmonary hypertension, hepatopulmonary syndrome, arrhythmia, and pulmonary embolism. This also helps in fluid management and vasopressor use in the resuscitation of patients with cirrhosis. Lung ultrasound (LUS) can help in differentiating pneumonia, effusion, and edema. Further, US guides interventions such as line placement, drainage of abdominal collections/abscesses, relief of tension pneumothorax, drainage of pleural and pericardial effusions, and biliary drainage in cholangitis. Additionally, its role is essential to assess liver masses foci of sepsis, for appropriate sites for paracentesis, and to assess for vascular disorders such as portal vein or hepatic vein thrombosis. Renal US can identify renal and postrenal causes of AKI and aid in diagnosis of prerenal AKI through volume assessment. In this review, we address the principles and methods of POCUS in hospitalized patients and in outpatients with cirrhosis and discuss the application of this diverse modality in clinical hepatology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".