Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic myelomonocytic leukemia (CMML) is a rare hematologic malignancy at the intersection of myelodysplastic (MDS) and myeloproliferative neoplasms, predominantly affecting older adults. Allogeneic hematopoietic cell transplantation (allo-HCT) remains the only curative option, yet its application is limited by the advanced age and comorbidities of most patients. Recent classification updates and refined prognostic tools, particularly molecularly integrated models like CPSS-Mol have enhanced patient stratification and informed transplant timing. The aim of this review is to highlight the evolving landscape of CMML management, with a focus on the role of allo-HCT. RECENT FINDINGS: Novel studies patients demonstrated that individualized transplant timing significantly improved life expectancy. Optimizing transplant outcomes hinges on several factors:managing pretransplant splenomegaly, choosing appropriate debulking strategies, selecting optimal donors, and tailoring conditioning regimens. New data favor treosulfan-based and thiotepa-busulfan regimens for their favorable toxicity and relapse profiles. Post-transplant, strategies like post-transplant cyclophosphamide (PTCy) for GVHD prophylaxis and emerging approaches to minimal residual disease (MRD) monitoring offer additional refinements in patient management. While no MRD studies are CMML-specific, extrapolation from MDS supports its role in relapse prediction. Innovative therapies, including hypomethylating agent combinations, venetoclax, targeted inhibitors, and immunotherapies are under active investigation, with potential to improve pre- and post-transplant outcomes. Advancements in molecular classification, dynamic prognostic tools, and therapeutic strategies are reshaping the CMML treatment paradigm. Personalized approaches that integrate genetic risk, patient fitness, and disease characteristics are enabling more effective transplant strategies, with the ultimate goal of extending survival and improving quality of life in this complex and historically difficult-to-treat malignancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".