Islet cell transplantation: the effects of COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 coronavirus is a worldwide epidemic. Estimates of the infection vary by country and region, and US reports over a quarter of the total COVID-19 cases, reported worldwide. COVID-19 has made a significant impact on organ transplantation, in general, and islet cell transplantation, in particular. Islet cell transplantation has been proven a viable cell replacement strategy for treatment of patients with impaired awareness of hypoglycemia and severe hypoglycemia and is now approved as standard of care in Canada, Europe, Japan and Australia. Clinical success of an islet transplant is largely dependent on the quality of a deceased donor pancreas. Hence, careful selection and testing of potential organ donors are of critical importance. The threat of COVID-19 transmission has either significantly slowed down or completely shut down islet transplant programs in most US transplant centers. MATERIALS AND METHODS: Literature regarding COVID-19 infection rates and mitigation strategies, National Institutes of Health, American Society of Transplantation and UNOS (United Network for Organ Sharing) recommendations regarding donor organ testing for SARS-CoV-2 and resource allocation were reviewed. CONCLUSIONS: Impact of local COVID-19 transmission and changing epidemiology of the disease, availability of resources that include protective equipment, donor procurement teams and adequate donor testing, impact of immunosuppression regiments on COVID-19 infection, as well as local regulations, are issues that should be critically assessed prior to reopening islet transplant programs.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".