Allogeneic Stem Cell Transplantation: The Relevance of Conditioning Regime Intensity for Myelodysplastic Syndromes (MDS)
Bibliographic record
Abstract
Allogeneic hematopoietic cell transplantation (alloHCT) is the sole curative therapy for myelodysplastic syndrome (MDS). While alloHCT clearly confers a significant survival advantage in high-risk MDS, it is less clear how the disease burden and impact of conditioning intensity impact survival. This review addresses critical issues surrounding this topic, emphasizing the unique cell biology of MDS and the evolving concepts of conditioning intensity compared to other diseases, including acute myeloid leukemia (AML). The review is structured around three interconnected themes. First, it clarifies the varying interpretations of conditioning intensity. Second, it examines the interplay between disease burden at transplant and conditioning intensity in determining outcomes, including a comparative analysis with acute myeloid leukemia (AML) to highlight similarities and differences. Third, it explores the relationship between conditioning regimen intensity and immune reconstitution, particularly focusing on the graft-versus-tumor (GvT) effect and its potential modulation by conditioning intensity. Understanding the stem cell target of conditioning regimens is emphasized, as the persistence of the underlying MDS stem cell necessitates a thorough understanding of this concept for improved therapeutic strategies.
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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 | 0.001 |
| 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 | 0.001 | 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".