Effect of HCV viral dynamics on treatment design: lessons learned from HIV
Bibliographic record
Abstract
Viral load measurements provide an indication of viral replication, and thereby serve as a valuable tool to guide the initiation of therapy and subsequent changes. Plasma human immunodeficiency viral load strongly predicts the rate of decrease in CD4+ lymphocyte count, and progression to AIDS and death. Furthermore, the efficacy of antiretroviral therapy can be assessed by monitoring changes in plasma human immunodeficiency viral load. Similarly, viral load provides valuable information about the natural history of the hepatitis C virus infection. Hepatitis C viral load can be used to predict the likelihood of response to standard interferon-alpha treatment and other interferon-alpha regimens and to monitor treatment efficacy. Increased understanding of the natural history of the hepatitis C virus infection and the nature of resistance to interferon-alpha therapy suggests that effective treatment regimens must maintain serum levels of interferon-alpha. Ideally, interferon-alpha serum levels should provide constant pressure on the virus and should prevent viral rebound, thereby avoiding continued viral replication and minimizing the potential for emergence of resistant quasi-species. Current regimens designed to address these points include early aggressive intervention, combination drug regimens, prolonged maintenance, and novel interferons. By enabling the design and rapid assessment of new treatment regimens, viral load measurement will revolutionize the clinical management of the hepatitis C virus infection, as it has the HIV.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".