Treatment outcomes following leukemic transformation in Philadelphia-negative myeloproliferative neoplasms
Bibliographic record
Abstract
Leukemic transformation (LT) is a rare but fatal complication of Philadelphia-negative myeloproliferative neoplasms (MPNs) for which optimal treatment strategies are not known. At our center, we have adopted a treatment approach for LT where patients within the transplant age group who have a reasonable fitness level are treated with curative intent and offered induction chemotherapy. Subsequently, those who respond and have a suitable donor are considered for allogeneic hematopoietic cell transplantation (HCT). In this study, we evaluated the clinical outcomes of this treatment approach in 75 patients with LT. The 2-year overall survival (OS) from the time of LT was 15%. A total of 39 patients (52%) were treated with curative intent (induction ± HCT) and had a 2-y OS of 26% compared with 3% in those noncuratively treated (P < .0001). In the curative intent group, 18 individuals (46%) achieved complete remission (CR) or CR with incomplete recovery and 12 (31%) reverted to a chronic MPN phase, with 17 patients undergoing HCT. Survival of patients posttransplant was significantly improved compared with those who responded to induction but were not transplanted (2-y OS of 47% vs 15%; P = .03). Thus, induction chemotherapy followed by HCT has the potential for long-term disease control in select patients with LT preceded by a MPN.
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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) | 0.000 | 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".