Hepatitis C Virus Infection and Hospital-Related Outcomes: A Systematic Review
Bibliographic record
Abstract
Background: People living with hepatitis C infection (HCV) have a significant impact on the global healthcare system, with high rates of inpatient service use. Direct-acting antivirals (DAAs) have the potential to alleviate this burden; however, the evidence on the impact of HCV infection and hospital outcomes is undetermined. This systematic review aims to assess this research gap, including how DAAs may modify the relationship between HCV infection and hospital-related outcomes. Methods: We searched five databases up to August 2022 to identify relevant studies evaluating the impact of HCV infection on hospital-related outcomes. We created an electronic database of potentially eligible articles, removed duplicates, and then independently screened titles, abstracts, and full-text articles. Results: A total of 57 studies were included. Analysis of the included studies found an association between HCV infection and increased number of hospitalizations, length of stay, and readmissions. There was less consistent evidence of a relationship between HCV and in-hospital mortality. Only four studies examined the impact of DAAs, which showed that DAAs were associated with a reduction in hospitalizations and mortality. In the 14 studies available among people living with HIV, HCV coinfection similarly increased hospitalization, but there was less evidence for the other hospital-related outcomes. Conclusions: There is good to high-quality evidence that HCV negatively impacts hospital-related outcomes, primarily through increased hospitalizations, length of stay, and readmissions. Given the paucity of studies on the effect of DAAs on hospital outcomes, future research is needed to understand their impact on hospital-related outcomes.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".