Ethical and Analytic Challenges With Genomic Sequencing of Relapsed Hematologic Malignancies Following Allogeneic Hematopoietic Stem-Cell Transplantation
Bibliographic record
Abstract
The implementation of precision medicine and next-generation sequencing technologies in the field of oncology is a novel approach being more widely studied and used in cases of high-risk primary and recurrent malignancies. Leukemias are the second most common cause of cancer-related mortality in children and the sixth most in adults. Relapsed leukemia represents a major component of the population that may benefit from genomic sequencing. However, ethical and analytic challenges arise when considering sequencing of biologic samples obtained from patients with relapsed leukemia following allogeneic hematopoietic stem-cell transplantation. Blood from the recipient after transplantation would include donor-derived cells and thus, genomic sequencing of recipient blood will interrogate the donor germline in addition to the somatic genetic profile of the leukemia cells and the recipient germline. This is a situation for which the donor will not have typically provided consent and may be particularly problematic if actionable secondary or incidental findings related to the donor are uncovered. We present the challenges raised in this scenario and provide strategies to mitigate this risk.
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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 | 0.134 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".