Findings from a Longitudinal Series of Continuing Education and Quality Improvement Programs in Myelofibrosis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».