Bibliographic record
Abstract
Ever since the first outbreaks of hepatitis in hemodialysis units in the late 1960s, a number of hepatotropic viruses transmitted by blood and other body fluids have been identified. This review summarizes the current state of knowledge regarding these blood-borne agents from an epidemiologic and preventive perspective. Data source and study selection were obtained from research and review articles related to the epidemiology of viral hepatitis in hemodialysis and indexed on Medline and Embase from 1965 to 2004. Hepatitis B virus (HBV) was the first significant hepatotropic virus to be identified in hemodialysis centers. HBV infection has been effectively controlled by active vaccination, screening of blood donors, the use of erythropoietin, and segregation of HBV carriers. To date, HBV remains an important cause of morbidity in endemic areas. Hepatitis delta virus is a defective virus that can only infect HBV-positive individuals. Hepatitis C virus is the most significant cause of non-A, non-B hepatitis and is mainly transmitted by blood transfusion. The introduction in 1990 of routine screening of blood donors for HCV contributed significantly to the control of HCV transmission. An effective HCV vaccine remains an unsolved challenge, however. Pegylation of interferon-alpha has made it possible to treat HCV-positive dialysis patients. Unexplained sporadic outbreaks of hepatitis by the mid-1990s prompted the discovery of hepatitis G virus and hepatitis GB virus C in 1995 and the TT virus in 1997. Although epidemiologic analyses revealed high prevalence rates of both viruses in the hemodialysis population, their exact role in liver disease has yet to be determined. The vigilant observation of guidelines on universal precaution and regular virologic testing are the cornerstones of the effective control of chronic hepatitis in the setting of hemodialysis.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".