Clinical Practice Guideline for Solid Organ Donation and Transplantation During the COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus 2019 (COVID-19) pandemic has disrupted health systems worldwide, including solid organ donation and transplantation programs. Guidance on how best to screen patients who are potential organ donors to minimize the risks of COVID-19 as well as how best to manage immunosuppression and reduce the risk of COVID-19 and manage infection in solid organ transplant recipients (SOTr) is needed. METHODS: Iterative literature searches were conducted, the last being January 2021, by a team of 3 information specialists. Stakeholders representing key groups undertook the systematic reviews and generation of recommendations using a rapid response approach that respected the Appraisal of Guidelines for Research and Evaluation II and Grading of Recommendations, Assessment, Development and Evaluations frameworks. RESULTS: The systematic reviews addressed multiple questions of interest. In this guidance document, we make 4 strong recommendations, 7 weak recommendations, 3 good practice statements, and 3 statements of "no recommendation." CONCLUSIONS: SOTr and patients on the waitlist are populations of interest in the COVID-19 pandemic. Currently, there is a paucity of high-quality evidence to guide decisions around deceased donation assessments and the management of SOTr and waitlist patients. Inclusion of these populations in clinical trials of therapeutic interventions, including vaccine candidates, is essential to guide best practices.
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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 | 0.029 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.021 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".