COVID-19 Among Patients With Hepatitis B or Hepatitis C: A Systematic Review
Bibliographic record
Abstract
Context: Hepatic manifestations of Coronavirus Disease 2019 (COVID-19) are common among people living with Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV). Objectives: This systematic review aimed to summarize the evidence on COVID-19 patients living with HBV or HCV co-infections. Data Sources: We searched multiple electronic databases and preprint servers from December 1, 2019, to August 9, 2020. Study Selection: Studies were included if they reported quantitative empirical data on COVID-19 patients living with HBV or HCV co-infections. Data Extraction: Descriptive analyses were reported, and data were synthesized narratively. The quality assessment was completed using the Joanna Briggs Institute critical appraisal tools. Results: Out of the 941 uniquely identified records, 27 studies were included. Of the eligible studies, 232 COVID-19 patients were living with HBV and 22 were living with HCV. Most patients were male, and the mean age was 49.8 and 62.8 years in patients living with HBV and HCV, respectively. Among the reported cases of SARS-CoV-2-HBV co-infection, the proportions of death were 4.7% and 15% in cross-sectional and case series/report studies, respectively. The death proportion was 8.3% among the reported cases of SARS-CoV-2-HCV co-infection. Among COVID-19 patients, 34.1% and 76.2% reported at least one comorbidity besides HBV and HCV infections, mainly hypertension and type 2 diabetes mellitus. The most common COVID-19-related symptoms in both HBV and HCV groups were fever, cough, dyspnea, fatigue, and gastrointestinal symptoms. Conclusions: While understanding the pathogenesis of SARS-CoV-2 requires further investigations, the careful assessment of hepatic manifestations and chronic infections, such as HBV and HCV upon the admission of COVID-19 patients could help reduce multimorbidity among HBV or HCV patients and lead to more favorable health outcomes among them.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".