Epidemiology of hepatitis C virus in HIV-infected patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will give an update on the prevalence of HIV/hepatitis C virus (HCV) coinfection, and describe recent trends in all-cause and cause-specific mortality. The focus is mainly on patients followed in clinics in high-income countries and their heterogeneity in terms of risk factors and clinical outcomes. RECENT FINDINGS: In countries that have introduced comprehensive preventive strategies for injection drug users, the prevalence of HIV/HCV coinfection has declined. Compared with HIV monoinfected patients, the mortality among HCV-coinfected patients remains markedly increased because of multiple risk factors, in particular among drug users. The spectrum of causes of death among coinfected has recently been described in large cohort studies. Around a quarter of all deaths were liver related, and the incidence has decreased in Western Europe and stabilized in Eastern Europe where AIDS remains the dominant cause of death. In North America, the incidence of end-stage liver disease has not decreased despite improvements in HIV care. HCV treatment seems to have had little effect thus far on mortality at the population level. SUMMARY: Despite a decreasing prevalence of HIV/HCV coinfection in many countries, coinfection remains an important clinical problem that requires a multidisciplinary approach including direct-acting antivirals for those at risk of liver-related death.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".