Findings from a Longitudinal Series of Continuing Education and Quality Improvement Programs in Myelofibrosis
Bibliographic record
Abstract
Background: Recent advances in myelofibrosis research have led to new medical treatment options such that oncologists must learn how to appropriately incorporate these new agents to optimally tailor treatment plans or sequence therapy. The first JAK inhibitor (JAKi) for myelofibrosis (MF) was approved by the FDA in 2011 with subsequent drug approvals in 2019, 2022, and 2023. Personalizing treatment planning requires molecular testing, prognostic scoring, and symptom assessment. Hematologists and oncologists may not consistently perform these tasks to customize treatment for patients with primary myelofibrosis (PMF) and secondary myelofibrosis (SMF). Effective shared decision-making (SDM) requires clinicians to be familiar with available treatment options, efficacy data, and side effect profiles so they may engage patients in treatment planning discussions. Methods: From 2021 to 2024, AXIS Medical Education and Q Synthesis launched a longitudinal series of continuing education activities and worked with 5 cancer centers in Maryland, California, Louisiana on MF-focused Quality Improvement (QI) projects. Clinician education focused on how to incorporate molecular testing, prognostic scoring, and patient symptoms to tailor treatment and assess response when patients are treated with JAKi. QI sites reviewed patient charts to assess baseline practice patterns and used Plan-Do-Study-Act (PDSA) cycles to improve clinical processes supporting the personalized care of patients with PMF and SMF. Post-intervention chart reviews were conducted to quantify changes in key process measures. Results: The educational content was delivered to a national audience of 3,217 clinicians where 56% gained knowledge and competence to evaluate clinical safety and efficacy of medical treatments; 52% learned how to incorporate prognostic risk scoring; 45% gained skills to personalize treatment based on patient factors, and 36% learned how to discuss and prioritize goals of treatment with patients. Compared to baseline, QI sites improved molecular testing (+17% improvement), documenting symptom scores using MPN-10 (+69% improvement), calculating prognostic scores (+34% improvement), incorporating JAKi to treat PMF (+35% improvement), and incorporating JAKi to treat SMF (+40% improvement). Oncologists also gained experience incorporating newer JAKi into treatment plans based on efficacy data, patient factors and response to previous therapies. Conclusions: Newer therapies for MF have expanded the options for oncologists to better customize care for patients. Tools, like the MPN-10, are readily available to clinicians but may be underutilized in practice. The findings from these continuing education and QI programs demonstrate that cancer clinicians can improve care for patients with PMF and SMF by incorporating appropriate testing and tailoring therapy based on prognostic scores and patient symptoms. This project was made possible through educational grants from Bristol Myers Squibb, CTI BioPharma Corp., a Sobi Company, GlaxoSmithKline, and Incyte.
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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 | 0.028 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".