Is allogeneic stem cell transplantation for myelofibrosis still indicated at the time of molecular markers and <scp>JAK</scp> inhibitors era?
Bibliographic record
Abstract
Abstract Objective The role of allogenic stem cell transplantation (ASCT) is still debated in myelofibrosis (MF). Methods A retrospective analyzed was performed to compare the outcome of 71 patients with intermediate‐2 or high‐risk Dynamic International Prognosis Scoring System+ (DIPSS+) primary (PMF) or secondary (SMF) myelofibrosis with an indication of ASCT as they ultimately underwent the procedure (n=34) or not (n=37). Results Five‐year overall survival (OS) was not statistically different between both groups (allograft: 52% vs no allograft: 34%, P=.12). However, progression to myelodysplastic syndrome or acute myeloid leukemia at 5 years was significantly lower in transplanted patients (14% vs 50%, P=.01). In univariate analysis, 5‐year OS was significantly higher for transplanted vs non‐transplanted patients with unfavorable karyotype (75% vs 0%, P=.001), SMF (71% vs 20%, P=.001) or high DIPSS+ score (46% vs 15%, P=.03). There was also a trend for better 5‐year OS in allografted patients with high JAK2V617F burden (>65%) (75% vs 8%, P=.07). Interestingly, the survival of patients who did not proceed to ASCT was dramatically increased by the use of ruxolitinib. Conclusions Not all intermediate‐2/high‐risk DIPSS+ MF patients benefit from ASCT, especially since the introduction of JAK2 inhibitors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".