COVID-19 and Liver Disease: An Evolving Landscape
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in significant worldwide morbidity and mortality. In this review, we examine the intricate relationships between COVID-19 and liver diseases. While respiratory manifestations of COVID-19 are well known, its impact and consequences in patients with liver diseases remain an area of ongoing investigation. COVID-19 can induce liver injury through various mechanisms and is associated with higher mortality in individuals with preexisting chronic liver disease. Mortality increases with the severity of chronic liver disease and the level of care required. The outcomes in patients with autoimmune hepatitis remain unclear, whereas liver transplant recipients are more likely to experience symptomatic COVID-19 but have comparable outcomes to the general population. Despite suboptimal immunological response, COVID-19 vaccinations are safe and effective in liver disease, although cases of autoimmune hepatitis-like syndrome have been reported. In conclusion, COVID-19 has significant implications in liver diseases; early recognition and treatments are important for improving patient outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".