Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a significant health problem, as it can lead to chronic active hepatitis, liver cirrhosis, and hepatic carcinoma. Patients undergoing hemodialysis treatment are at increased risk of contracting HCV and other viral infections. This is primarily due to their impaired cellular immunity, underlying diseases, and blood exposure for a prolonged period. Transmission of viral hepatitis, and in particular HCV in dialysis units, has been showing a progressive increase worldwide, ranging between 5% in some western countries and up to 70% in some developing countries. The annual rate of HCV seroconversion in Saudi Arabia is 7% to 9%, while its prevalence is variable between 15% and 80%. This prevalence remained at almost 50% in recent years, despite the further increase in number of patients with end-stage renal disease and the expansion of dialysis services. The most prevalent genotypes in Saudi Arabia are genotype 4 followed by genotypes 1a and 1b, whereas genotypes 2a/2b, 3, 5, and 6 are rare. Genotypes 1 and 4 were associated with different histological grades of liver disease. Mixed infections with more than one genotype were observed in some studies. Isolation of dialysis machines and infected patients, together with strict application of infection-control policies and procedures and continuous education and training of nursing staff, remain the cornerstone in prevention and control of the spread of HCV infection in dialysis units. Interferon (INF)-alpha or pegylated INF, alone or in combination with ribavirin, have shown great promise in the treatment of chronic HCV in dialysis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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; 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".