The effect of COVID-19 on patients with liver cirrhosis: A systematic review and meta-analysis of retrospective studies (Preprint)
Bibliographic record
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is long-lasting and has an adverse effect on liver function. However, the impact of COVID-19 on the outcome of patients with liver cirrhosis has not been consistently clear. OBJECTIVE We aimed to conduct a systematic review and meta-analysis to explore whether COVID-19 negatively impacts cirrhosis patients. METHODS We conducted a systematic search of the PubMed/Medline, Embase, and Cochrane Library databases to compare cirrhosis patients with and without COVID-19. Two authors independently performed data extraction and quality evaluation using the Newcastle‒Ottawa Scale. Data pooling was conducted using random-effects or fixed-effects models based on the heterogeneity of the included studies. RESULTS A total of 13 studies were included that involved 949 patients with cirrhosis and COVID-19 and 15,196 patients with cirrhosis only. Of the 13 studies, ten were studies among hospitalized patients, and three were studies among discharged patients. COVID-19 infection increased the mortality, ICU (Intensive Care Unit) admission rate, length of hospital stay and incidence of acute-on-chronic liver failure (ACLF), Child‒Pugh C and hepatic encephalopathy among hospitalized patients with liver cirrhosis. However, COVID-19 infection did not affect the mortality rate or the incidence of Child‒Pugh C among patients with cirrhosis after discharge. CONCLUSIONS In hospitalized patients with cirrhosis, infection with COVID-19 may be a potential risk factor for adverse clinical outcomes. However, COVID-19 infection seems to have no effect on patients with cirrhosis after discharge. It is recommended that clinicians pay more attention to the prevention and treatment of COVID-19 in patients with preexisting liver cirrhosis. CLINICALTRIAL PROSPERO CRD42023429256; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=429256
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".