COVID-19: A global transplant perspective on successfully navigating a pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has rapidly evolved and changed our way of life in an unprecedented manner. The emergence of COVID-19 has impacted transplantation worldwide. The impact has not been just restricted to issues pertaining to donors or recipients, but also health-care resource utilization as the intensity of cases in certain jurisdictions exceeds available capacity. Here we provide a personal viewpoint representing different jurisdictions from around the world in order to outline the impact of the current COVID-19 pandemic on organ transplantation. Based on our collective experience, we discuss mitigation strategies such as donor screening, resource planning, and a staged approach to transplant volume considerations as local resource issues demand. We also discuss issues related to transplant-related research during the pandemic, the role of transplant infectious diseases, and the influence of transplant societies for education and disseminating current information.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".