How Early Is Too Early? Use of Lineage‐Specific Chimerism in Early Detection of Donor‐Derived Malignancy After Allogeneic Stem Cell Transplant: A Case Report
Bibliographic record
Abstract
Allogeneic stem cell transplant is critical for treatment of certain hematologic malignancies. However, it has significant risks including relapsed malignancy, infection, and graft versus host disease. Rarely, de novo malignancy can arise from donor cells. Chimerism analysis is used to monitor engraftment and predict rejection or disease relapse. Our patient underwent an allogeneic transplant for myelodysplastic syndrome but had persistent pancytopenia despite donor lymphocyte infusion. This was due to donor-derived malignancy, which was predicted by loss of a satellite marker on chimerism analysis 6 months prior. This could have allowed earlier intervention and underscores the importance of detailed chimerism monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".