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Record W2981513517 · doi:10.1016/j.bbmt.2019.10.017

Maintenance Tyrosine Kinase Inhibitors Following Allogeneic Hematopoietic Stem Cell Transplantation for Chronic Myelogenous Leukemia: A Center for International Blood and Marrow Transplant Research Study

2019· article· en· W2981513517 on OpenAlexaff
Zachariah DeFilipp, Richard G. Ancheta, Ying Liu, Zhen‐Huan Hu, Robert Peter Gale, David S. Snyder, Harry C. Schouten, Matt Kalaycio, Gerhard Hildebrandt, Celalettin Üstün, Andrew Daly, Siddhartha Ganguly, Yoshihiro Inamoto, Mark R. Litzow, Jeff Szer, Mary Lynn Savoie, Nasheed Hossain, Mohamed A. Kharfan‐Dabaja, Mehdi Hamadani, Ran Reshef, Ashish Bajel, Kirk R. Schultz, Shahinaz M. Gadalla, Aaron T. Gerds, Jane L. Liesveld, Mark Juckett, Rammurti T. Kamble, Shahrukh K. Hashmi, Hisham Abdel‐Azim, Melhem Solh, Ulrike Bacher, Hillard M. Lazarus, Richard F. Olsson, Jean‐Yves Cahn, Michael R. Grunwald, Bipin N. Savani, Jean A. Yared, Jacob M. Rowe, Jan Černý, Naeem Chaudhri, Mahmoud Aljurf, Amer Beitinjaneh, Sachiko Seo, Taiga Nishihori, Jack W. Hsu, Muthalagu Ramanathan, Edwin P. Alyea, Uday Popat, Ronald Sobecks, Wael Saber

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Cancer Institute
KeywordsMedicineChronic myelogenous leukemiaDasatinibNilotinibImatinibInternal medicineTransplantationMaintenance therapyLeukemiaImatinib mesylateHematopoietic stem cell transplantationTyrosine-kinase inhibitorGraft-versus-host diseaseOncologyMyeloid leukemiaChemotherapyCancer

Abstract

fetched live from OpenAlex

It remains unknown whether the administration of tyrosine kinase inhibitors (TKIs) targeting BCR-ABL1 after allogeneic hematopoietic cell transplantation (HCT) is associated with improved outcomes for patients with chronic myelogenous leukemia (CML). In this registry study, we analyzed clinical outcomes of 390 adult patients with CML who underwent transplantation between 2007 and 2014 and received maintenance TKI following HCT (n = 89) compared with no TKI maintenance (n = 301), as reported to the Center for International Blood and Marrow Transplant Research. All patients received TKI therapy before HCT. The majority of patients had a disease status of first chronic phase at HCT (n = 240; 62%). The study was conducted as a landmark analysis, excluding patients who died, relapsed, had chronic graft-versus-host disease, or were censored before day +100 following HCT. Of the 89 patients who received TKI maintenance, 77 (87%) received a single TKI and the other 12 (13%) received multiple sequential TKIs. The most common TKIs used for maintenance were dasatinib (n = 50), imatinib (n = 27), and nilotinib (n = 27). As measured from day +100, the adjusted estimates for 5-year relapse (maintenance, 35% versus no maintenance, 26%; P = .11), leukemia-free survival (maintenance, 42% versus no maintenance, 44%; P = .65), or overall survival (maintenance, 61% versus no maintenance, 57%; P = .61) did not differ significantly between patients receiving TKI maintenance or no maintenance. These results remained unchanged in multivariate analysis and were not modified by disease status before transplantation. In conclusion, our data from this day +100 landmark analysis do not demonstrate a significant impact of maintenance TKI therapy on clinical outcomes. The optimal approach to TKI administration in the post-transplantation setting in patients with CML remains undetermined.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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".

Quick stats

Citations35
Published2019
Admission routes1
Has abstractyes

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