Bibliographic record
Abstract
Approximately 25% to 35% of HIV-infected persons in developed countries are coinfected with hepatitis C virus (HCV). HCV liver disease is accelerated by HIV coinfection, especially at low CD4 cell counts. Highly active antiretroviral therapy (HAART) dramatically reduces HIV-related mortality, and liver disease has emerged as a major cause of death in HIV/HCV-coinfected persons. Anti-HCV therapy with pegylated interferon plus ribavirin can cure HCV infection in up to 40% of coinfected patients; however, only approximately 10% of coinfected patients are considered candidates. Hence, HCV therapy cures approximately 4% of coinfected patients. Eleven cohort studies have shown that HAART is associated with a reduced rate of progression of HCV liver disease, and 4 of these studies have demonstrated a reduction in liver-related mortality. Although offering HCV therapy to the few eligible HIV/HCV-coinfected patients is important, early initiation of HAART in coinfected patients has a greater public health impact in reducing liver-related mortality than in curing HCV infection in approximately 4% of these 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".