Use of COVID-19 vaccines in patients with liver disease and post-liver transplantation
Bibliographic record
Abstract
Patients with chronic liver disease (CLD) and liver transplant recipients are at increased risk of morbidity and mortality from coronavirus disease 2019 (COVID-19). Although several studies demonstrated the safety and efficacy of COVID-19 vaccines in the general population, data in CLD patients and liver transplant recipients are lacking. Two COVID-19 vaccines were approved by the Saudi Food and Drug Authority and rolled out to several million recipients in Saudi Arabia. These vaccines are mRNA-based vaccine BNT162b2 from Pfizer/BioNTech and adenovirus-based AZD1222 from Oxford/AstraZeneca from three manufacturing sites (EU Nodes, Serum Institute of India, and South Korea Bio). The Saudi Association for the Study of Liver diseases and Transplantation (SASLT) has reviewed the available evidence and issued interim recommendations for COVID-19 vaccination in CLD and liver transplant recipients. Since there is no evidence contradicting the safety and immunogenicity of the currently approved COVID-19 vaccines in patients with CLD and hepatobiliary cancer and liver transplant recipients, the SASLT recommends vaccination in those patient populations. CLD and hepatobiliary cancer patients and liver transplant recipients should be prioritized depending on the risk factors for severe COVID-19. In transplant recipients, the optimal timing of vaccination remains unknown; however, immunization is recommended after the initial immunosuppression phase. Patients with CLD and liver transplant candidates or recipients should be closely monitored after COVID-19 vaccination. These patient populations should be included in future clinical trials to provide further evidence on the efficacy and safety of COVID-19 vaccines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".