Efficacy of Vaccine Protection Against COVID-19 Virus Infection in Patients with Chronic Liver Diseases
Bibliographic record
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) has resulted in significant morbidity and mortality worldwide. Vaccination against coronavirus disease 2019 is a useful weapon to combat the virus. Patients with chronic liver diseases (CLDs), including compensated or decompensated liver cirrhosis and noncirrhotic diseases, have a decreased immunologic response to coronavirus disease 2019 vaccines. At the same time, they have increased mortality if infected. Current data show a reduction in mortality when patients with chronic liver diseases are vaccinated. A suboptimal vaccine response has been observed in liver transplant recipients, especially those receiving immunosuppressive therapy, so an early booster dose is recommended to achieve a better protective effect. Currently, there are no clinical data comparing the protective efficacy of different vaccines in patients with chronic liver diseases. Patient preference, availability of the vaccine in the country or area, and adverse effect profiles are factors to consider when choosing a vaccine. There have been reports of immune-mediated hepatitis after coronavirus disease 2019 vaccination, and clinicians should be aware of that potential side effect. Most patients who developed hepatitis after vaccination responded well to treatment with prednisolone, but an alternative type of vaccine should be considered for subsequent booster doses. Further prospective studies are required to investigate the duration of immunity and protection against different viral variants in patients with chronic liver diseases or liver transplant recipients, as well as the effect of heterologous vaccination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".