Systematic Scoping Review of Studies Reporting Unexpected Donor-Derived Abnormalities from Recipients of Allogeneic Hematopoietic Cell Transplantation: A Proposed Framework for Donor Disclosure
Bibliographic record
Abstract
Allogeneic hematopoietic cell transplantation (HCT) is used increasingly to treat blood and immune-based disorders. Post-transplantation testing of HCT recipients can lead to unexpected molecular, cytogenetic, and other information in donor-derived cells, raising questions regarding the potential impact on donor health. This study was conducted to identify the breadth of donor-derived abnormalities identified by testing HCT recipients and to determine the extent to which disclosure and donor follow-up are described. A systematic search and scoping review were conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines in OVID MEDLINE and Embase (1947 to May 24, 2021). We identified 38 studies (63 donor-recipient pairs) addressing nonleukemic abnormalities to complement existing literature describing donor cell leukemia and donor-derived myelodysplasia. Donors were unrelated adults (n = 20), related family members (n = 28), cord blood donors (n = 6), or not reported (n = 9). Acquired cytogenetic, molecular, and morphologic abnormalities were reported. Donor origin was confirmed by cytogenetic analysis via karyotyping, fluorescence in situ hybridization, single tandem repeat PCR, and other techniques. A disease in donor-derived cells was described in 35 recipients (56.5%). Despite the relevance for testing and disclosure to donors, only 22 cases (32%) mentioned donor follow-up, and in 5 cases the donor developed a disease associated with the identified abnormality. Unrelated donor disclosure was mentioned in 3 of 26 cases (12%), with the findings reported back to the registry. Incidental abnormalities identified in transplanted donor cells may contribute to the post-transplantation risk of illness in the recipient and may be relevant to donor health. A framework for donor disclosure is proposed that incorporates consideration of analytic validity of the testing, potential significance of the finding, and the extent to which the abnormality is actionable. Adoption of effective processes to safeguard both donor and recipient health outcomes related to this issue is needed.
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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 | 0.067 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".