NOTTO guidelines for vaccine induced thrombotic thrombocytopenia in organ donation and transplantation
Bibliographic record
Abstract
From the context of organ donation, COVID-19 vaccine-induced thrombotic thrombocytopenia (VITT) is important as there is an ethical dilemma in utilizing versus discarding organs from potential donors succumbing to VITT. This consensus statement is an attempt by the National Organ and Tissue Transplant Organization (NOTTO) apex technical committees India to formulate the guidelines for deceased organ donation and transplantation in relation to VITT to help in appropriate decision making. VITT is a rare entity, but a meticulous approach should be taken by the Organ Procurement Organization's (OPO) team in screening such cases. All such cases must be strictly notified to the national authorities like NOTTO, as a resource for data collection and ensuring compliance withprotocols in the management of adverse events following immunization. Organs from any patient who developed thrombotic events up to 4 weeks after adenoviral vector-based vaccination should be linked to VITT and investigated appropriately. The viability of the organs must be thoroughly checked by the OPO, and the final decision in relation to organ use should be decided by the expert committee of the OPO team consisting of a virologist, a hematologist, and atreating team. Considering the organ shortage, in case of suspected/confirmed VITT, both clinicians and patients should consider the risk-benefit equationbased on available experience, and an appropriate written informed consent of potential recipients and family members should be obtained before transplantation of organs from suspected or proven VITT donors.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".