Fatigue in chronic liver disease: New insights and therapeutic approaches
Bibliographic record
Abstract
The management of fatigue associated with chronic liver disease is a complex and major clinical challenge. Although fatigue can complicate many chronic diseases, it is particularly common in diseases with an inflammatory component. Fatigue can have both peripheral (i.e., neuromuscular) and central (i.e., resulting from changes in neurotransmission within the brain) causes. However, fatigue in chronic liver disease has strong social/contextual components and is often associated with behavioural alterations including depression and anxiety. Given the increasing awareness of patient-reported outcomes as important components of treatment outcomes and clinical research, there is a growing need to better understand and manage this poorly understood yet debilitating symptom. Although several pathophysiological mechanisms for explaining the development of fatigue have been generated, our understanding of fatigue in patients with chronic liver disease remains incomplete. A better understanding of the pathways and neurotransmitter systems involved may provide specific directed therapies. Currently, the management of fatigue in chronic liver disease can involve a combined use of methods to beneficially alter behavioural components and pharmacological interventions, of which several treatments have potential for the improved management of fatigue in chronic liver disease. However, evidence and consensus are lacking on the best approach and the most appropriate biochemical target(s) whilst clinical trials to address this issue have been few and limited by small sample size. In this review, we outline current understanding of the impact of fatigue and related symptoms in chronic liver disease, discuss theories of pathogenesis, and examine current and emerging approaches to its treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".