Optimal Timing of Allogeneic Stem Cell Transplantation for Primary Myelofibrosis
Bibliographic record
Abstract
Primary myelofibrosis (PMF) is a chronic myeloproliferative neoplasm characterized by cytopenias, splenomegaly, and risk of leukemic transformation. In light of newer therapies, such as ruxolitinib, that are not curative but improve quality of life, the timing of transplantation needs more in-depth analysis to determine which patients would benefit from an early versus a delayed transplantation strategy. Because prospective clinical trials are impractical for diseases with only one curative option, such as PMF, we developed a Markov cohort model to simulate the long-term disease trajectory in patients with PMF and predict the optimal timing of transplantation stratified by Dynamic International Prognostic Scoring System (DIPSS) risk. In this decision model, a hypothetical cohort of patients begins in the alive with PMF state and can transition monthly to other health states. Transition probabilities were acquired from published literature. We performed probabilistic analyses by jointly varying all model parameters over 1000 simulations. Irrespective of DIPSS risk, all patients with PMF benefited from transplantation with respect to life expectancy gained. Life expectancy gains from transplantation peaked at 9.7 months (95% confidence interval [CI], 9.5 to 9.9 months) from diagnosis in patients with high-risk disease and at 16.6 months (95% CI, 16.4 to 16.8 months) from diagnosis in patients with intermediate-2 disease. Patients with intermediate-1 risk had a delayed peak in net gain in life expectancy at 20.5 months (95% CI, 20.2 to 20.7 months). Patients with low-risk disease had a greater net gain in life expectancy the longer that transplantation was delayed; this trend plateaued at 29 to 45 months. Our modeling suggests that preparation for transplantation is indicated upfront for patients diagnosed with intermediate-2 risk and high-risk PMF, whereas this can be delayed for low-risk or intermediate-1 risk disease.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".