Impact of recent COVID-19 infection on liver and kidney transplantation – a worldwide meta-analysis and systematic review
Bibliographic record
Abstract
Introduction: The shortage of suitable donor organs represents an ongoing global challenge for organ transplantation. During the COVID-19 pandemic, the number of transplantable organs was especially limited. To date, the impact of recent coronavirus-19 (COVID-19) infection on liver and kidney transplant recipients has not been systematically analyzed, which is essential for the development of future transplant management. Methods: We conducted a systematic review and meta-analysis to assess the clinical outcomes of recent COVID-19 infection in the donor (1) or the recipient (2). A total of 17 studies were considered for systematic review, seven of these were included for meta-analysis. Results: Transplantation of COVID-19 positive donors did not result in an impaired graft survival for liver or kidney transplantation up to 180-days of follow up. Additionally, a positive COVID-19 donor status was not associated with decreased overall survival in kidney transplant recipients within 180 days of transplantation. Nevertheless, an association was found with decreased overall survival in liver transplant recipients within the 180-day follow-up period. Discussion: However, the heterogeneity of studies investigating COVID-19 infection of the recipient did not allow a classification of the significance of COVID-19 positive recipients. Conclusively, a COVID-19 positive donor status should not be considered as an exclusive factor for declining a suitable liver or kidney for transplantation. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024562551.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".