Bibliographic record
Abstract
Viral hepatitis is one of the current “hot topics” in medical circles, among clinical laboratory professionals, and even among the general public. Newly heightened awareness of the magnitude of the problem of hepatitis C viral infections in the United States population, together with the availability of new therapies for hepatitis C, have stimulated initiatives designed to identify and treat the large numbers of patients infected with this agent. At the same time, demands for the elimination of hepatitis viruses from the blood supply in the United States have led over the past year to the onset of nucleic acid-based testing of blood products and the appearance of solvent-detergent-treated plasma. Still more recently, press reports of the discovery of a new viral agent, Sen V, which is being touted as the long-sought agent responsible for most cases of non-A, non-E hepatitis have stirred both excitement and controversy within the medical and laboratory communities. Thus, the appearance of a new book devoted to the known hepatitis viruses and the diagnosis, therapy, and prevention of viral hepatitis is both topical and timely. This book begins with a short introduction by the editor, Steven Specter, which summarizes the major characteristics of the known hepatitis viruses and the diseases they cause. The next six chapters, each written by internationally recognized experts in their respective fields, provide detailed discussions of the biology, pathogenesis, epidemiology, clinical description, and diagnosis of hepatitis viruses A through E and the GB viruses (including the hepatitis G agent). The eighth chapter, which should be of particular interest and value to clinical chemists and other laboratory professionals, provides a comprehensive discussion of the laboratory procedures currently available for the diagnosis of viral hepatitis. The final three chapters are devoted to the …
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.035 | 0.004 |
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".