HBV coinfection and in-hospital outcomes for COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Since December 2019, there are 30 million confirmed cases of a novel coronavirus disease (COVID-19) secondary to severe acute respiratory syndrome coronavirus 2. As of 2020, hepatitis B virus (HBV) affects more than 200 million people worldwide. Both are caused by viral agents. The short-term mortality rate from COVID-19 is much higher than that of HBV. Objective: We sought to understand the impact of HBV coinfection on hospitalized patients with COVID-19. Search Methods: Searches of the literature were conducted in the PubMed, Cochrane Library, and Embase electronic databases. Selection Criteria: We included cohort studies and randomized studies with information on rates of mortality and intensive care unit (ICU) admission from individuals coinfected by HBV and COVID-19. Data Collection and Analysis: Data from six cohort studies with 2,015 patients were collected between January and April 2020, and the results were analyzed by meta-analysis. Main Results: = 2,015; adjusted OR = 0.79, 95% CI 0.31-1.98). During their hospital stay, coinfected patients did not appear to have an increased hospital length of stay or risk of hepatitis B reactivation. Conclusions: This systematic review and meta-analysis provides support that HBV is not a significant risk factor for serious adverse outcomes among patients hospitalized for COVID-19 infection.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".