Hepatitis C virus infection in dialysis patients
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a global health problem, common worldwide, leading to acute and chronic hepatitis and its consequences of hepatocirrhosis and hepatocellular carcinoma. Patients on hemodialysis belong to the high-risk group of HCV infection. The prevalence of HCV infection in dialysis patients ranges from 4% to more than 70% in some countries. The main reasons for such a high incidence of infections are a high prevalence of HCV infection in the general population, lack of standard infection precautions and effective vaccination, inadequate disinfection procedures of dialysis machines and other medical equipment, as well as spread of infection from patient to patient, especially in dialytic centers with a high percentage of infected patients. The diagnostic procedures useful in the evaluation of HCV infection are detection of anti-HCV antibodies, identification of HCV RNA, counts of virus copies, and identification of its genome. From the 6 major genotypes and multiple subtypes of the HCV, genotypes 1a and 1b are the most common in Europe and Japan, and 1b is responsible for more severe liver disease and aggressive course leading to liver fibrosis. Antiviral therapy of HCV+ dialysis patients with interferon-alpha (INF-alpha) gives slightly better results than in the general population, but is poorly tolerated and associated with side effects. Although ribavirin in not recommended for dialysis patients, the addition of small doses of this compound to pegylated INF is discussed, especially for patients in whom previous infection treatment failed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads 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".