Myelodysplastic Neoplasms (MDS): The Current and Future Treatment Landscape
Bibliographic record
Abstract
Myelodysplastic neoplasms (MDS) are a heterogenous clonal disorder of hemopoietic stem cells characterized by cytomorphologic dysplasia, ineffective hematopoiesis, peripheral cytopenias and risk of progression to acute myeloid leukemia (AML). Our understanding of this disease has continued to evolve over the last century. More recently, prognostication and treatment have been determined by cytogenetic and molecular data. Specific genetic abnormalities, such as deletion of the long arm of chromosome 5 (del(5q)), TP53 inactivation and SF3B1 mutation, are increasingly associated with disease phenotype and outcome, as reflected in the recently updated fifth edition of the World Health Organization Classification of Hematolymphoid Tumors (WHO5) and the International Consensus Classification 2022 (ICC 2022) classification systems. Treatment of lower-risk MDS is primarily symptom directed to ameliorate cytopenias. Higher-risk disease warrants disease-directed therapy at diagnosis; however, the only possible cure is an allogenic bone marrow transplant. Novel treatments aimed at rational molecular and cellular pathway targets have yielded a number of candidate drugs over recent years; however few new approvals have been granted. With ongoing research, we hope to increasingly offer our MDS patients tailored therapeutic approaches, ultimately decreasing morbidity and mortality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".