COVID‐19 vaccination timing and kidney transplant waitlist management: An international perspective
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has created unprecedented challenges for solid organ transplant programs worldwide. The aim of this study is to assess an international perspective on challenges faced by kidney transplant programs. METHODS: We administered an electronic survey instrument from January 3, 2021 to June 8, 2021 to staff at transplant programs outside the United States that comprised of 10 questions addressing the management of kidney transplant candidates with asymptomatic COVID-19 infection or unvaccinated who receive an organ offer. RESULTS: Respondents (n = 62) represented 19 countries in five continents. Overall, 90.3% of respondents encourage vaccination on the waiting list and prior to planned living donor transplant. Twelve percent of respondents reported that they have decided to inactivate unsensitized candidates (calculated panel reactive antibody, cPRA <80%) until they received the two doses of vaccination, and 7% report inactivating candidates who have received their first vaccine dose pending receipt of their second dose. The majority (88.5%) of international respondents declined organs for asymptomatic, nucleic acid testing (NAT)+ patients during admission without documented prior infection. However, 22.9% of international respondents proceeded with kidney transplant in NAT+ patients who were at least 30 days from initial diagnosis with negative chest imaging. CONCLUSIONS: Practitioners in some countries are less willing to accept deceased donor organs for waitlist candidates with incomplete COVID-19 vaccination status and to wait longer before scheduling living donor transplant, compared to United States practices. Access to vaccinations and other resources may contribute to these differences. More research is needed to guide the optimal approach to vaccination before and after transplant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".