Next-generation sequencing-based minimal residual disease monitoring in patients receiving allogeneic hematopoietic stem cell transplantation for acute myeloid leukemia or myelodysplastic syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: The monitoring of minimal residual disease (MRD) has important clinical implications in both the pre and postallogeneic stem cell transplant (SCT) setting in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Next-generation sequencing (NGS) is a rapidly improving technology whose application to the monitoring of MRD is an active area of research. We aim to describe existing methods of MRD in AML and MDS, with a focus on the utility of NGS in patients undergoing SCT. RECENT FINDINGS: Flow cytometry and quantitative PCR have been recommended by the European Leukemia Net as the preferred methods of MRD in AML and MDS, but these methods have limitations in cases without a disease-defining phenotype and genotype. Clinical trials are currently ongoing to assess the use of NGS in the setting of SCT for MDS and AML. Few studies have so far assessed the optimal method of MRD monitoring in the posttransplant setting. SUMMARY: The optimal method for the monitoring of MRD in AML and MDS both pre and post transplant may require more than one technology. NGS holds great promise for the monitoring of MRD, with prospective trials currently ongoing to evaluate its efficacy in this regard.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".