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Record W2907303596 · doi:10.1182/blood-2018-99-119002

Survival Advantage to Allogeneic Transplant in Patients with Myelofibrosis with Intermediate-1 or Higher DIPSS Score

2018· article· en· W2907303596 on OpenAlexaff
Krisstina Gowin, Karen K. Ballen, Kwang Woo Ahn, Zhen‐Huan Hu, Ying Liu, Lucia Masárová, Srđan Verstovšek, Maria Coakley, Tania Jain, Andrew Kuykendall, Rami S. Komrokji, Martha Wadleigh, Sarah Patches, Murat O. Arcasoy, Michael Green, Malathi Kandarpa, Moshe Talpaz, Haris Ali, Vikas Gupta, Rebecca Devlin, Laura C. Michaelis, Gabriela Hobbs, Brady L. Stein, Ashley Pariser, Aaron T. Gerds, Kierstin Kuber, Raajit K. Rampal, Edwin P. Alyea, Uday Popat, Ronald Sobecks, Bart L. Scott, Ruben A. Mesa, Wael Saber

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineTransplantationMyelofibrosisInternational Prognostic Scoring SystemInternal medicineAdverse effectProportional hazards modelSurgeryBone marrowMyelodysplastic syndromes

Abstract

fetched live from OpenAlex

Abstract Introduction: Allogeneic hematopoietic stem cell transplantation (HCT) is the only curative therapy for myelofibrosis (MF). Consideration of HCT is recommended by international working groups and national guidelines for MF patients (pts) age <70 with intermediate-1 with adverse indicators, intermediate-2 or high-risk disease by the Dynamic International Prognostic Scoring System (DIPSS) for MF- a recommendation made in the absence of clear data indicating the optimal timing of HCT for MF. In this large multicenter retrospective study, we analyze overall survival in MF pts treated with and without HCT. Methods: Disease characteristics, treatments, and outcome data from MF pts receiving non-transplant therapy at 14 US academic medical centers between 2000-2014 were retrospectively collected. MF pts who underwent HCT were identified from the Center for International Blood and Marrow Transplant Research (CIBMTR). The Cox proportional hazards model was used. The reference time point (time zero) was time of referral for the non-transplant (non-HCT) arm and the time of transplant for the HCT arm. The main effect variable (HCT vs. non-HCT) violated the proportionality assumption where comparing to non-HCT, mortality was higher with HCT in early time period from time zero but then was lower in late time period; therefore, the comparison is presented as early time period and late time period. The Cox model identified 14 months from time zero as the ideal cut point to define early and late time periods. The proportionality assumption is satisfied within each of these two periods. Results: A total of 1377 and 551 pts were included in the non-HCT and HCT arms, respectively (Table 1). In the overall cohort, survival was higher with non-HCT vs. HCT in early time period (relative risk [RR]: 0.34, P< .0001, Figure 1D), but in late time period survival was lower with non-HCT vs. HCT (RR: 2.37, P< 0.001) (Table 2). In the DIPSS low-risk MF group, while survival was higher with non-HCT vs. HCT in the early time period (RR: 0.19, P=0.007, Figure 1A), survival was lower with non-HCT in the late time period, but the latter did not reach statistical significance (RR: 1.45, P=0.39). In the DIPSS intermediate-1 risk group, a survival advantage was present with non-HCT treatments vs. HCT in the early time period (RR: 0.27, P < .0001, Figure 1B), however survival was lower with non-HCT in the late time period (RR: 3.13, P < .0001). Similarly, in those with DIPSS intermediate-2 and high-risk MF, survival advantage was observed with non-HCT in the early time period (RR: 0.41, P< .0001, Figure IC), but survival was lower with non-HCT in the late time period (RR: 2.82, P < .0001). Conclusion: A long-term survival advantage with transplant was observed for pts with intermediate-1 or higher risk MF, but at the cost of potential early mortality. The magnitude of benefit increased as DIPSS risk score increased. Although this retrospective study has limitations, the results have an impact on clinical practice by suggesting that transplantation could be considered earlier in the disease course and supports the recommendation for consideration of HCT in the setting of intermediate-1 risk MF. Disclosures Gowin: Incyte: Consultancy, Other: Scientific Advisory Board, Speakers Bureau. Verstovsek:Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Consultancy; Italfarmaco: Membership on an entity's Board of Directors or advisory committees. Ali:Incyte Corporation: Membership on an entity's Board of Directors or advisory committees. Gupta:Novartis: Consultancy, Honoraria, Research Funding; Incyte: Research Funding. Gerds:Celgene: Consultancy; Apexx Oncology: Consultancy; CTI Biopharma: Consultancy; Incyte: Consultancy.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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 designObservational
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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