Risk of Severe COVID-19 Increased by Metabolic Dysfunction-associated Fatty Liver Disease
Bibliographic record
Abstract
BACKGROUND: The prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) in coronavirus disease-2019 (COVID-19) patients and whether it affects the outcomes of COVID-19 requires investigation. GOALS: The aim was to determine the prevalence of MAFLD among COVID-19 patients and its influence on the outcomes of COVID-19 by meta-analysis. METHODS: Our study protocol has been registered on PROSPERO (CRD42021242243). The studies published on PubMed, Embase, Cochrane Library, and Web of Science before March 11, 2021 were screened. The Newcastle-Ottawa scale (NOS) and Agency for Healthcare Research and Quality scale were used to assess the quality of the studies. Pooled analysis was conducted using the software RevMan version 5.3 and Stata version 15.0 SE. The stability of the results was assessed by sensitivity analysis. Publication bias was evaluated using funnel plots, Egger test, and trim-and-fill analysis. RESULTS: Seven studies covering 2141 COVID-19 patients were included. It was confirmed that MAFLD increased the risk of severe COVID-19 (odds ratios: 1.80, 95% confidence interval: 1.53-2.13, P<0.00001). No association was found between the presence of MAFLD and the occurrence of COVID-19 death. The pooled prevalence of MAFLD among COVID-19 patients was 36% (95% confidence interval: 0.23-0.49, P<0.00001). Sensitivity analysis confirmed that the initial results were stable. CONCLUSIONS: MAFLD can increase the incidence of severe COVID-19, but the correlation between MAFLD and COVID-19 death has not been confirmed. Further investigation is needed to explore the possible mechanism of this association. Since MAFLD is common among patients infected with SARS-CoV-2, more care should be given to COVID-19 patients with underlying MAFLD.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".