Outcome of Patients with Metabolic-Associated Fatty Liver Disease Who Are Infected with SARS-CoV-2: A Meta-Analysis.
Bibliographic record
Abstract
BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is excess fat accumulation in the liver due to metabolic syndrome. Coronavirus disease 2019 (COVID-19) is an infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 not only attacks the respiratory system but also involves systemic and extra-pulmonary organ disorders, including liver disorders. This review evaluates the severity of COVID-19, mortality, and length of hospital stays of patients with MAFLD who were infected with SARS-CoV-2. METHODS: Literature searches were conducted through various online databases. The risk of bias assessment was conducted by two researchers using the Newcastle Ottawa Scale tool for NRSI studies, and any discrepancies were resolved by another team member. The meta-analysis was performed using Revman 5.4.1 and results were presented in forest plot by calculating the pooled odds ratio or mean difference between the MAFLD and non-MAFLD groups from the evaluated studies with a 95% CI. RESULTS: The results of the meta-analysis using a fixed-effect model from seven studies showed that COVID-19 patients with MAFLD were associated with a higher mortality compared to those without MAFLD (OR 1.41, 95% CI 1.19-1.69, p=0.01, I2 48). However, there were no differences in COVID-19 severity (OR 3.12, IK95% 0.89-11.03, p=0.08, I2 92) and length of hospital stay (MD 1.27, CI95% 0.03-2.52, p=0.04, I2 80) between the two groups. CONCLUSION: MAFLD patients infected with SARS-CoV-2 were associated with higher mortality than non-MAFLD patients, but they were not associated with greater severity of COVID-19 nor a longer duration of hospitalization.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.064 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".