Fatigue in chronic hepatitis <scp>C</scp> infection: Understanding patients' experience from a cognitive‐behavioural perspective
Bibliographic record
Abstract
OBJECTIVES: Fatigue is a leading concern of patients with chronic hepatitis C virus (HCV) infection. Despite its clinical significance, fatigue in HCV is poorly understood and therefore invariably under-treated. A cognitive-behavioural approach offers a framework to understand and treat fatigue, but the characteristics of fatigue in chronic HCV infection have not been documented from a cognitive-behavioural perspective. This study captured the common and unique aspects of fatigue from a cognitive-behavioural perspective in individuals with HCV infection and clinically significant fatigue. DESIGN: Cross-sectional, qualitative using a critical realism approach. METHODS: Fourteen individuals (64% women; age >18 years) participated in semi-structured interviews. The interviews documented the features, course, and perceived antecedents of fatigue; fatigue-specific cognitions; fatigue management behaviours; and the functional impact of fatigue. RESULTS: Participants' descriptions included the aspects of fatigue that have been targets of cognitive-behavioural therapy in other medical conditions, including attributing fatigue to the illness; expectation of chronicity; low control; and fatigue-driven coping. There were also components of fatigue experience that appear to be unique characteristics of fatigue related to HCV, including predominantly physical fatigue; high acceptance of fatigue; and liver-protective diet as a fatigue management behaviour. CONCLUSIONS: This was the first study to document the experience of fatigue in chronic HCV infection in a cognitive-behavioural framework. The findings suggest that the cognitive-behavioural approach can be applied to fatigue in chronic HCV infection. This would open an avenue to alleviate fatigue and thus improve the primary patient-reported outcome of the disease. STATEMENT OF CONTRIBUTION: What is already known on this subject? Fatigue is a key patient-reported outcome measure of chronic hepatitis C virus (HCV) infection. Fatigue management is not part of the standard care, because fatigue is poorly characterized in this population. What does this study add? A cognitive-behavioural approach can be applied to understand fatigue in HCV infection. Identified aspects of fatigue (antecedents, consequences, cognitions, behaviours) that can be treatment targets. Cognitive-behavioural therapy would open a new treatment avenue to alleviate fatigue in HCV infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".