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Record W3212899144 · doi:10.1182/blood-2021-147376

Decision Analysis of Allogeneic Stem Cell Transplantation for Primary Myelofibrosis

2021· article· en· W3212899144 on OpenAlexaff
Christopher Cipkar, Srishti Kumar, Kednapa Thavorn, Natasha Kekre

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMyelofibrosisMedicineCohortTransplantationRuxolitinibNatural historyInternal medicinePediatricsOncologyBone marrow

Abstract

fetched live from OpenAlex

Abstract Introduction: Primary myelofibrosis (PMF) is a chronic myeloproliferative neoplasm characterized by cytopenias, splenomegaly and a risk of leukemic transformation. In light of newer therapies such as ruxolitinib that are not curative but can improve quality of life, the timing of transplant needs more in-depth analysis to determine which patients would benefit from an early versus delayed transplant strategy. Methods: We developed a Markov cohort model to simulate the long-term disease trajectory in patients with PMF and predict the optimal timing of transplant stratified by a Dynamic International Prognostic Scoring System (DIPSS) risk. Our model consisted of five health states including alive with PMF, alive after leukemic transformation, alive after transplant, alive after relapse and death. Transition probabilities between health states were acquired from published literature on the natural history of the disease and outcomes following transplantation. The model was run over a patient's lifetime until all patients transitioned to the death state. We used a cycle length of one-month to represent the natural progression of PMF. The structure of the Markov model is delineated in Figure 1. In this decision model, a hypothetical cohort of patients begins in the Alive-PMF state and can transition after each monthly cycle to other health states. Patients could remain in an alive state for any number of cycles without transitioning to another health state, indicated by the arrow wheels. We performed probabilistic analyses by jointly varying all model parameters over 1000 simulations and calculated 95% confidence intervals (CI) for the model outcome. Results: Regardless of DIPSS risk, all patients with PMF benefited from a transplant with respect to life expectancy gained (Figure 2). Life expectancy gains from a transplant among patients with high-risk disease peak at 9.7 months (95% CI: 9.5-9.9) from diagnosis, while patients with intermediate-2 disease have a peak gain in life expectancy at 16.6 months (95% CI: 16.4-16.8). Intermediate-1 DIPSS risk patients have a more delayed time frame where the net gain in life expectancy from transplant begins to slow at 20.5 months (95% CI: 20.2-20.7). Patients with low risk DIPSS had greater net gain in life expectancy the longer transplant was delayed; this trend plateaued at 29 to 45 months, when thereafter net gain in life expectancy begins to be lost (Figure 1). Conclusion: Our modeling suggests that transplant processes including donor selection and pre-transplant work-up are indicated upfront for patients diagnosed with intermediate-2 and high risk PMF, while this can be delayed for patients with low or intermediate-1 risk disease. This model should provide clinicians with guidance on when to refer eligible patients with PMF for transplantation. Figure 1 Figure 1. Disclosures Kekre: Novartis: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Celgene: Consultancy, Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.015
GPT teacher head0.261
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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