Quality of life, depression, and cytokine patterns in patients with chronic hepatitis C treated with antiviral therapy
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of chronic hepatitis C and antiviral therapy on health-related quality of life (HRQoL), depression symptoms and cytokine patterns. METHODS: Twenty HCV+ patients treated with peginterferon plus ribavirin were enrolled in this cohort study and invited to complete SF-12 and BDI questionnaires prior to (T0) and at the end of the treatment (T1). HCV-RNA, serum levels of ALT, AST, haemoglobin, ferritin and IFN-gamma, TNF-alpha, IL-2, IL-4, IL-6 and IL-10 were evaluated at T0 and T1. The questionnaire results were correlated to biochemical and cytokine parameters. RESULTS: Two patients (1%) dropped out and 18 HCV patients composed the final sample (11 males (61.1%); mean age 42.5+/-11.9 yr; mean disease duration 9.7+/-6.9 yr). Between T0 and T1 ALT (p=0.02), AST (p=0.052) HCV-RNA (P=0.0002) and haemoglobin levels decreased (p=0.0003), whereas ferritin level increased (P=0.003). Also, at T1 all cytokine levels were augmented. Regarding depression status, at T0 10 patients (55.5%) scored above to the BDI questionnaire (suggesting clinically significant depression), whereas at T1 14 patients scored 10 or above (77.7%). At T1 the mean BDI score increased, but this difference was not significant. Regarding HRQoL, the majority of patients had T0 summary scores < or = 50. At T1 HRQoL changed and scores decreased in 66.7% of the patients. A correlation was observed between the T0 level of ferritin and the amount of change in BDI and SF-12 mental score between T0 and T1 (Spearman rho = -0.56 and +0.61, respectively) and IL-4 level at T0 and the change in BDI and SF-12 mental scores (Spearman rho = -0.49 and +0.45, respectively). CONCLUSION: BDI, SF-12, IL-4 and ferritin are good tools to predict the appearance of depressive symptoms and worsening of the quality of life in the HCV+ population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".