Prioritization of Therapeutic Targets and Trial Design in Cirrhotic Portal Hypertension
Bibliographic record
Abstract
Portal hypertension (PH) is the main driver of cirrhosis decompensation, the main determinant of death in patients with cirrhosis. PH results initially from increased intrahepatic vascular resistance. Subsequently, increased inflow from splanchnic vasodilation and increased cardiac output lead to a further increase in portal pressure (PP). Reducing PP in cirrhosis results in better outcomes. Removing the cause of cirrhosis might improve PP. However, this is a slow process and patients may continue to be at risk of decompensation. Additionally, for some chronic liver diseases, such as nonalcoholic fatty liver disease (NAFLD), etiological treatments are not yet available. Therefore, there is a need to develop better therapies specifically aimed at reducing PP. For over 35 years, the mainstay of such therapy has been the use of nonselective beta-blockers (NSBBs) that act by reducing portal venous inflow. Recently, many drugs (mainly targeting intrahepatic mechanisms) have shown promise in preclinical and early clinical studies and may act alone or synergistically with NSBBs in reducing PP in cirrhosis. The objective of this position paper is to propose a novel framework for the design of clinical trials (phase 1, 2, and 3) in patients with cirrhosis and PH and to prioritize targets and pharmacological therapies in this setting. We have focused the discussion on patients with compensated cirrhosis. The paper summarizes discussions held at The American Association for the Study of Liver Diseases (AASLD) Industry Colloquium in January 2018, with the participation of clinical and translational investigators, regulatory professionals, and industry partners.
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 | 0.074 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".