The search for disease-modifying agents in decompensated cirrhosis: From drug repurposing to drug discovery
Bibliographic record
Abstract
Patients with decompensated cirrhosis are currently managed through targeted strategies aimed at preventing or treating specific complications. In contrast, a disease-modifying agent should, by definition, be aimed at globally addressing 'decompensated cirrhosis'. To be defined as a disease-modifying agent in decompensated cirrhosis, interventions need to demonstrate an unequivocal benefit on the course of disease in well-designed and adequately powered randomised clinical trials with hard endpoints (i.e. patient survival). These trials also need to define the target population, dosage and timing of administration, factors guiding treatment, temporary or permanent stopping rules, transferability to daily clinical practice, cost-effectiveness, and global treatment access. By eliminating the underlying cause of cirrhosis, aetiologic treatments can still influence the course of decompensated disease by halting or slowing down disease progression or even inducing reversion to the compensated state. In contrast, there remains an unmet clinical need for disease-modifying agents which can antagonise key pathophysiological mechanisms of decompensated cirrhosis, such as portal hypertension, gut translocation, circulatory dysfunction, systemic inflammation, and immunological dysfunction. However, in the last few years, the repurposing of "old drugs" that have already been prescribed for more limited indications in hepatology or for other diseases has provided a few candidates, including human albumin, statins, and poorly absorbable oral antibiotics, which are under further evaluation in large-scale randomised clinical trials. New disease-modifying agents are also expected to be identified in the next decade through the systematic repurposing of existing drugs and the development of novel molecules which are currently undergoing pre-clinical or early clinical testing.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".