Consensus Statement on Organ Donation from COVID-Positive Deceased Donors-Indian Society of Organ Transplantation, Liver Transplant Society of India and Indian Society for Heart and Lung Transplantation
Bibliographic record
Abstract
COVID has drastically impacted organ donation across the world, leading to untold misery for thousands of patients who have been waiting for organs. Early rules on the use of organs from COVID positive or affected donors were stringent due to the fear of spread of disease or thrombotic complications in patients who received these organs. However much has changed in the past two years. Most of our adult population has either been infected with COVID, or has received two doses of vaccine, or both. The current variant, despite being more infective, is associated with mild disease, especially in those who have been vaccinated Our armamentarium against severe COVID has improved dramatically in the past year- we have effective vaccines, monoclonal antibodies for treatment of mild COVID in high risk patients and post exposure and antiviral prophylaxis and treatment which can substantially reduce the risk of severe COVID requiring ICU admission. The risk of transmission of COVID infection has to be balanced against the risk of patients dying with end organ disease. We will have to learn to live with COVID- this also means investigating whether organs from donors who are, or have been COVID positive can be used with acceptable risk –benefit in selected patients with end stage organ failure. This document is a summary of evidence and information regarding donor screening for SARS-CoV-2 and considerations for organ acceptance from donors with a history of COVID-19.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".