Endpoints and design of clinical trials in patients with decompensated cirrhosis: Position paper of the LiverHope Consortium
Bibliographic record
Abstract
Management of decompensated cirrhosis is currently geared towards the treatment of complications once they occur. To date there is no established disease-modifying therapy aimed at halting progression of the disease and preventing the development of complications in patients with decompensated cirrhosis. The design of clinical trials to investigate new therapies for patients with decompensated cirrhosis is complex. The population of patients with decompensated cirrhosis is heterogeneous (i.e., different etiologies, comorbidities and disease severity), leading to the inclusion of diverse populations in clinical trials. In addition, primary endpoints selected for trials that include patients with decompensated cirrhosis are not homogeneous and at times may not be appropriate. This leads to difficulties in comparing results obtained from different trials. Against this background, the LiverHope Consortium organized a meeting of experts, the goal of which was to develop recommendations for the design of clinical trials and to define appropriate endpoints, both for trials aimed at modifying the natural history and preventing progression of decompensated cirrhosis, as well as for trials aimed at managing the individual complications of cirrhosis.
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 | 0.682 | 0.592 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.024 | 0.030 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".