Genetic Findings of Potential Donor Origin following Hematopoietic Cell Transplantation: Recommendations on Donor Disclosure and Genetic Testing from the World Marrow Donor Association
Bibliographic record
Abstract
Following hematopoietic cell transplantation (HCT), recipients are subjected to extensive genetic testing to monitor the efficacy of the transplantation and identify relapsing malignant disease. This testing is increasingly including the use of large gene panels, which may lead to incidental identification of genetic and molecular information of potential donor origin. Deciphering whether variants are of donor origin, and if so, whether there are clinical implications for the donor can prove challenging. In response to queries from donor registries and transplant centers regarding best practices in managing donors when genetic mutations of potential donor origin are identified, the Medical Working Group of the World Marrow Donor Association established an expert group to review available evidence and develop a framework to aid decision making. These guidelines aim to provide recommendations on predonation consenting, postdonation testing of recipients, and informing and managing donors when findings of potential donor origin are identified in recipients post-transplantation. It is recognized that registries will have different access to resources and financing structures, and thus whenever possible, we have made suggestions on how recommendations can be adapted.
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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 | 0.038 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".