HBV coinfection and in-hospital outcomes for COVID-19: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it