Hematopoietic Cell Transplantation as Curative Therapy for Patients with Myelofibrosis: Long-Term Success in all Age Groups
Bibliographic record
Abstract
Myeloproliferative neoplasms (MPN) are chronic marrow disorders with variable prognoses. Most patients with polycythemia vera, essential thrombocythemia, or even primary myelofibrosis (PMF) are successfully treated with conservative strategies for years or even decades, and recent data suggest that even in patients with high-risk disease, in particular those with PMF, life expectancy can be extended by treatment with janus kinase (JAK2) inhibitors. However, none of those modalities are curative, and after marrow failure develops, the disease "accelerates," or transforms to acute leukemia, the only option able to effectively treat and, in fact, cure MPN is allogeneic hematopoietic cell transplantation (HCT). Outcome is superior if HCT is performed before leukemic transformation occurs. Several reports document survival in unmaintained remission beyond 10 years. The most recent analyses show reduced regimen-related mortality (less than 10% or even 5% at day 100) and progressively improved survival with both HLA-identical sibling and unrelated donors. The development of low/reduced-intensity conditioning regimens has contributed to the improved success rate and has allowed successful HCT in patients in their seventh and even eighth decade of life. We propose, therefore, that HCT should be offered to fit patients in these age groups and should be covered by their respective insurance carriers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".