Severity and Mortality in Covid-19 With Hepatitis B Co-Infection A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis investigate the severity and mortality in COVID-19 patients co-infected with Hepatitis B Virus (HBV). Utilizing established databases and adhering to PRISMA guidelines, 15 studies were included after a rigorous selection and data extraction process, with quality assessed using the Newcastle–Ottawa Scale (NOS). Statistical analysis was conducted using Review Manager (RevMan) software. The findings indicate a significantly higher risk of severe clinical outcomes and mortality in COVID-19 patients with HBV co-infection compared to those without HBV. Among patients with both HBV and COVID-19, 30.2% developed severe clinical outcomes, compared to 19.5% in COVID-19 patients without HBV. Mortality rates were also higher in the co-infected group (4.9%) compared to those with only COVID-19 (3.4%). The study notes considerable heterogeneity among the included studies, as indicated by high I2 values. These results align with previous findings on the impact of chronic liver diseases on COVID-19 outcomes but contrast with some studies suggesting a potential protective effect of HBV. The analysis also considers the complexities of managing COVID-19 in HBV patients, particularly the risks associated with corticosteroid therapy and HBV reactivation, while acknowledging limitations such as variability in methodologies and potential publication biases. The study underscores the heightened severity and mortality risks for COVID-19 patients with HBV co-infection, emphasizing the need for tailored management strategies and further research into the mechanisms and optimal treatment protocols for this patient population.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".