Advances in the diagnosis and management of clinically significant portal hypertension in cirrhosis: A narrative review
Bibliographic record
Abstract
Clinically significant Portal hypertension (PH), defined by a hepatic venous pressure gradient (HVPG) greater than 10 mmHg, is a key predictor of decompensation events in cirrhosis, leading to variceal hemorrhage, ascites, and hepatic encephalopathy. This narrative review explores the pathophysiology of PH in cirrhosis, evaluates diagnostic methods for identifying clinically significant PH (CSPH), and discusses guideline-driven strategies to prevent initial and further decompensation. While HVPG remains the gold standard for diagnosing CSPH, non-invasive tools such as liver stiffness measurement and spleen stiffness measurement are increasingly used for initial risk stratification. The combined use of these tools reduces the proportion of patients in the diagnostic "grey zone". Endoscopic ultrasound-guided portal pressure gradient is an emerging diagnostic tool that requires further validation. Non-selective beta-blockers are the cornerstone of primary prophylaxis for decompensation, and their combination with endoscopic variceal ligation is the first-line therapy for secondary prophylaxis of recurrent esophageal variceal bleeding. Statins show promise in reducing PH and preventing decompensation while further studies are still needed. This review also discusses the indications for preemptive transjugular intrahepatic portosystemic shunt and its role in managing refractory ascites and variceal bleeding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".