COVID-19 Severity and Mortality Among Chronic Liver Disease Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Pre-existing comorbid conditions in COVID-19 patients are risk factors for developing severe disease and death. We aimed to determine the association of chronic liver disease (CLD), a comorbid condition, with severity of disease and death among COVID-19 patients. METHODS: We searched for studies reporting COVID-19 outcomes among CLD and non-CLD patients in databases including Medline, EMBASE, ScienceDirect, Google Scholar, and Cochrane Library from inception of the pandemic until February 2022. Risk of bias assessment was conducted by using the Newcastle-Ottawa Scale for assessing the quality of nonrandomized studies in meta-analyses. We conducted a meta-analysis with a random-effects model and reported pooled odds ratios (ORs) with 95% CIs. RESULTS: We included 40 studies with 908,032 participants. Most studies were conducted in China and the US. COVID-19 patients with CLD had significantly higher odds of having a severe form of COVID-19 (pooled OR = 2.44; 95% CI, 1.89-3.16) and death (pooled OR = 2.35; 95% CI, 1.85-3.00) when compared with COVID-19 patients without CLD. CONCLUSION: The presence of CLD is significantly related to adverse clinical outcomes among COVID-19 patients in terms of severity and mortality. Clinicians should develop a comprehensive intervention plan to manage these high-risk patients and reduce COVID-19-related deaths.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".